{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kats 201 - Forecasting with Kats\n",
    "\n",
    "\n",
    "This tutorial will introduce time series modeling and forecasting with Kats. We will show you how to build forecasts with different Kats models and how to do parameter tuning and backtesting using Kats.  The complete table of contents for Kats 201 is as follows:\n",
    "\n",
    "1. Forecasting with Kats Base Models     \n",
    "    1.1 SARIMA     \n",
    "    1.2 Prophet     \n",
    "    1.3 Holt-Winters     \n",
    "2. Forecasting with Kats Ensemble Model\n",
    "3. Multivariate Model Forecasting\n",
    "4. Hyperparameter Tuning\n",
    "5. Backtesting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We provide two types of tutorial notebooks\n",
    "- **Kats 101**, basic data structure and functionalities in Kats (this tutorial)  \n",
    "- **Kats 20x**, advanced topics, including advanced forecasting techniques, advanced detection algorithms, `TsFeatures`, meta-learning, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Forecasting with Kats Base Models\n",
    "\n",
    "\n",
    "\n",
    "In this part, we will demonstrate the forecasting workflow with the following models with `air_passengers` data set:\n",
    "1. SARIMA\n",
    "2. Prophet,\n",
    "3. Holt-Winters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by loading the `air_passengers` data set into a `TimeSeriesData` object.  This code is essentially the same as the code in our introduction to the `TimeSeriesData` object in the Kats 101 Tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "warnings.simplefilter(action='ignore')\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "from kats.consts import TimeSeriesData\n",
    "air_passengers_df = pd.read_csv(\"../kats/data/air_passengers.csv\")\n",
    "\n",
    "# Note: If the column holding the time values is not called time, you will want to specify the name of this column.\n",
    "air_passengers_df.columns = [\"time\", \"value\"]\n",
    "air_passengers_ts = TimeSeriesData(air_passengers_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because each of our time series models follow the `sklearn` model API pattern, the code for each of the next three examples is quite similar.  We initialize the model with its parameters and then call the `fit` and `predict` methods.  The only difference between each of these examples are the model-specific parameters.  We can then use the `plot` method to visualize our forecast in each case.\n",
    "\n",
    "The values we choose for each of our paremeters in these examples are basically arbitrary.  Later in this tutorial, we will show you how to pick the right parameters for a model in Kats using hyperparameter tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 SARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Fb4wOvA1EuB3kDIeNS2YWY1EV3vm4NuRrrBYVt1cbdLDXdbP+eWRGyZH5XQkhhBBCxJAnTEC2qIr/oFqSuD0aarck1+H0kZFmJTsjhdnTCti0sy5CvbEy6JHTXp8epqvHyCEBWQghhBCiD16fTs8KC/8OcvICstPjC9rBNXeQAS6fXYLLo7F518nQLzQG38nC3ypuZHawAAnIQgghhBB98oapQU72DrLLrQdavBmGQafTS0aaPyBPKM2iKM9BxeEw3SwU/+7zYMRq2MhQJQFZCCGEEKIPXi1CDfIgSxUGw+XxBYaEuD0aPs0g/dQOMkBulp2OrtADeTaLSpdrcJ0sOl2+QDgfiSQgCyGEEEL0QtMNDJ2gjhFgBuTk7KRquoFPO72r3XGqdVtm2umAnOGwBXojd9ffVm9VdR34tODv0ykBWQghhBDizKVp4UOwqijohoGehFZvmhZ8SM7cKc5wWAOvZaRZA8G5O6tVxenyYkSYtted16dT29AVshPtdPtGbIs3kIAshBBCCNErTTcin0czlKT0Qvb6gkdfmzvF6d12kNMdNpwuX0jAVxUFTY+ujrjL5aPL5aOx1RV4TTvV4s0yQlu8gQRkIYQQQoheabrBiYZOfvrbD2locQW/qYCmJ77MwqfpYJxOyJ2BHeRuJRanwnJnuHpjxYjqgGFHl4e0VCvNbZ7ADwJen4YygjtYgARkIYQQQohe6bpB9clOTjY7eTtkhHNyxk17vMGjr9tP7SBn9KhBBsIe1DOv0ZemNjcOuwVN1+k6dY/BtogbDiQgCyGEEEL0QtOMwC7sBzvrglukJWnctMvtC2o719HlQ1XAYbcEXstI89cjhzuoZ1EVnH2MnPb6dJwuHyk2CzaLSku7J/C6MZKnhCABWQghhBCiVz5Np+vUYTeny8fWPd2GbygkZQfZ5dGDukiYPZC7d9o4vYMcWmJhs/TdycLp9gWu57BbqG9xYhgGXS4fVnVkR8iR/d0JIYQQQgySz6fT5faRlW6jrCiddz6qCXSAUFBCWqAlgtPtCzok19HlDeqBDN1qkMPtIFtUXH0MC2nv9ATKOCwWFa9P9x/ac3qxjuAOFiABWQghhBCiV55TpQaZaTYun11CdX0XlcfbAFDVxE+VMwyzi0S3EotuU/RM6b3UIFstCm6v1murt6Y2N6kpp0s2VFWhrcOD061hG8E9kEECshBCCCFEr8yd03SHjbnTC0lLtQYO66mKkvBDa5pugGGgKt1rkL1BQ0LAPxAkNcUStgZZURQMHXwRykO8Ph2n219/bEqzW6lrduLx6iO6xRtIQBZCCCGE6JVX0+l0+shIs5Fis3DReUVs399Ac5sbSxKm6Xl9Oj03fjucPtK7DQkxpafZInaxQPGXj4TjdPuC2siBP3C7PVrkntAjiARkIYQQQoheeL06na7TAfRTs0owDHh/e61/3LS391reWPP59KDDeLph+A/p9ahBBv9kvXCH9AAwIvdCbu/0EO4cnoIS1QS+4S70Rw0hhBBCCBHg9mp0Ok8fgivISaW0KJ1jJzqxqAruBJdYON3Bgdfp8mEYhNQgg7+TRXtvO8gRDhg296g/NmWm25IyWjvRZAdZCCGEECICXTfo6vL6A2i3HdpMh40Op9e/g5zgLhYNrcHhtT3MFD1TRpotbA0y+Hshu9yhu8s+zd+1o3v9cfevsY3wDhYgAVkIIYQQQ0BXV1eylxCWbhh0nQqR3Xdo09NsdDq9gcNuiRoW4vXpdHR6SLGdjnCdYabomTIctsAY6p6sqorTHVoe4nSF1h+faSQgCyGEECKp3n33XXJzc9mxY0eylxJC04zAQI2MbofgMhzW04M2FNAStIvc6fSCogTVIJs1xuEP6Vlxe3U8Yeqkreahux5cHg2UkV9G0RsJyEIIIYRIqn379uHxePjVr36V7KWE0PTTY6a7D+JId9jocvkCLdcStYPc1OYK6UHc0ccOMhB2ap7FEr7EorPLi22Et3Hry5n93QshhBAi6RoaGgB47rnnqKurS/Jqgmndxkx3D6Bm8Oxyef07yAkIyLpu0NzmwWEP3ik227j17IMMp9ccrg5ZVRQ0wwjZ/e50+WJaZ/zaB8dZ9daRmF0vESQgCyGEECKpGhoasFgseDwefve73yV7OUE0PXyJhVnO4C9vUNAiDNyIpS6XD03TUdXQHWSbVQ17qC6jl2l6fkpQqzfjVM11LEdJ7zvSTE1DZ8yulwgSkIUQQgiRVI2NjZSVlXH11Vfzm9/8Bo/Hk+wlBei6QZfLi0VVsHfrHGHuzHY6vWAkZge5rcMdEo4BOrt8YcsrADLSTgX5CJ0sMIKHhZhDSLpP6RushhYXeVmpMbteIkhAFkIIIURSNTQ0UFBQwJ133smJEyf461//muwlBXh9/pZnGWm2oINx6d1qexUFNC3+w0LqW9wh5RXgD78ZYQ7oQbcgH2lYiGLg7bb77fHqMR0Eomk6TW1u8rLtMbtmIkhAFkIIIURSmQH5qquuYsqUKTzyyCNDZlqbx6fT5fKFBFDznzuc/t1lT5yHhbg8Ptye8LXBHV3hp+gBpKXaUIi8g6wqSlAnC7dXC/pBYLCa2twYBuRlSUAWQgghhIiaGZBVVeWOO+7gww8/ZPPmzcleFgBen0aX0xfUwQK67yCfGhYS54Dc0eWNGFw7urykRyixsKgKaanWiDXIVouKu1sni84uL9YwZRwD1dDiApAd5O5aWlq46aabmDp1KtOmTWPTpk00NTWxaNEiJk+ezKJFi2hubgb8ReF33HEH5eXlnHvuuXz88cfxXJoQQgghhojGxkby8/MBuO2228jKyuKPf/xjklfl5/We2kHuEUBTbCpWi0JHlw9VDT7oFg+NrW7stvCxzV9iET4gg3+oSaQdZKtFwdltB7nTHdsOFoGALDvIp915551cffXV7Nu3jx07djBt2jSWL1/OFVdcwYEDB7jiiitYvnw5AK+++ioHDhzgwIEDrFixgttvvz2eSxNCCCHEEOD1emltbaWgoACAjIwMpk6dyvHjx5O8Mj+v5u+D3HMIh6IoZJyapmdRFbxxDMiGYfin56WEdqnQNB2nWwscxgsnw2GjI0wfZACLRcXZbQe5yxn7gGy1KGSlp8TsmokQt4Dc2trKu+++y9KlSwFISUkhJyeH1atXs2TJEgCWLFnCqlWrAFi9ejW33XYbiqJw4YUX0tLSQm1tbbyWJ4QQQoghoLGxESAQkAHy8vJoampK1pKCeLwanRF2aNMd/p1ZNc4B2ePV0XQjbGeJjjA9mnvKSLNGHDdtURV8mv/6Hq8Wto3cYDS0uMjLTo3pNRMhbgH58OHDFBYW8rWvfY3zzz+fr3/963R2dlJXV0dxcTEAo0ePDjQEr66uZsyYMYGvLysro7q6Ol7LE0IIIcQQYA4JGYoBWdcN2js8GAYhNchwety0qoBXi233h+5cYcZBmwJT9Hopschw2GiP2AeZQKs3j0+HGOfY+hYXBTnDq8UbQOT9+EHy+Xx8/PHHPProo8ybN48777wzUE5hUnrMEo/GihUrWLFiBQAnTpygpqYmZmseSerr65O9hBFBnmNsyHOMDXmOsSHPMTZi9Rz3798P+MsIzP+m2+12Ghsbk/7feK9P58RJf4BXtC5O1p1AUfy7rgA2VaOp1UNz40k6nD6qq7393imN5jk2NDtpb3Whu0NjW21tFwCGt4OmhvC72BY8dHR5aaw/ETZ3tTu9VFW78foMWpvb0V2Rw3Z/1Td1UZKXSWtzPbW1vrjtJMf6z3XcAnJZWRllZWXMmzcPgJtuuonly5dTVFREbW0txcXF1NbWMmrUKABKS0uD6o2qqqooLS0Nue6yZctYtmwZAHPmzKGkpCRe38KwJ88mNuQ5xoY8x9iQ5xgb8hxjI5bPcerUqYHrjRkzhtbWVoqKirBYQutuE6XL5cNicwNQNCqflLR0QCEn019Pm5vTxpETDeQVFGHp8DCqKD/sNLu+9PUc2z3NFNo0UsP0QD7S4A+GxaNHkVeQHvbrC/K9aHoz6VkFYa+htnvIL8im0+klz+cgMy029cKdTi8uj05ZcR7ZuYUUFxdiscTv+Fssfz/GbZWjR49mzJgxgZ8M169fz/Tp01m8eDErV64EYOXKlVx//fUALF68mKeffhrDMNi8eTPZ2dmBUgwhhBBCjEyRSiwMw6ClpSVJq/Lz+nQ6Tx1g85dY+Hc/zVKKDIeNTpcP/dQ/x2PctGEYdHR5Iwbvjq7oapCBiAf1FMVfa93h9GGLYYA1O1hIiUUPjz76KF/60pfweDxMnDiRJ598El3Xufnmm3niiScYN25cYFrONddcw9q1aykvLyctLY0nn3wynksTQgghxBBgBmSzzRv4AzJAU1NT0OuJ5tN0ulynanzTbKBAeqoVp1sjLdVKusOGYYDT5Q+e8Rg3HTigF6E0waxB7tllozuzPrmjyxs2rFotCk63/zBiuEl9A3U6IDtids1EiWtAnjlzJh9++GHI6+vXrw95TVEUHnvssXguRwghhBBDTGNjIxkZGdjtp/vkmqE42Qf1nG4fTpf/gFyGw4rLo1GY6+BITRtpqdbTY5ydPlJsFjQ99p0sejugB/7Qm2q3YO1l5zcQkCP0QrZYVDq6vPg0PVBfHQuBgJxtx+WN/yjuWJJJekIIIYRIGnOKXnfdd5CTyeXWcLp9WFQFe4oFFMjJTEFVVXTdCOzadji9KApBI5tjpcvV+8G23sZMmwJBPuI0PQW3VyPWLSwaWlxkpNnC1j0PdRKQhRBCCJE0Qzoge7RTQzjMAKpgs6oU5Nj90/Ucp4On1aLQFaHGdzDaOj2k9DK4o6XdTXZG74fqzPVHHBaiKvg0A2JcITJcW7yBBGQhhBBCJNFQDshuj0aX00uGw4puQIpVRVEU8rJT8fr0bjvI/ulzXe7Y7iD3dUAPoKHV3WcIddgtqErkEgtFUdB1AzXGqbBBArIQQgghRP81NjaGHMTLyckBkhuQNU1H0ww6nD7S02zouhEYwZzhsGG1qjhS/QG50+nF2mNkcyx4vDq+Xibb+TSdljY3+dm9h1BzLHZHL8NCNM0YUIu6yNfTaW6VgCyEEEII0W/hdpCtVivZ2dmBMdTJ4NV0FIXAmGmtW0BWVYVRuQ503R9eO5z+OmFd12M6ctrl0XqtCm5qdWMQXRs1cyx2JIW5qaSmxC4gN7W50Q0olIAshBBCCBE9j8dDW1tbSECG5I+b9vkMDE7tIDus6LoRVAucm21H15VT46b9wdNAiWlA7uuAXn/6DGek2SIe0ouH4dwDGSQgCyGEECJJzB3ioRiQvT4d3TC67SDr2GynY5PDbgXFCN6ZNfwDN2KlvY8Deo2t/hCaH01AdlgjHtKLBwnIQgghhBADYAbkcMNA8vPzkxqQPV4Nt1vHMPzlCboOKd0CskVVUBWFdIeVzlPBM5at3gzDoL2vA3otLiyqQk4fXSyAPmuQYy2wtkx73x8egiQgCyGEECIpwo2ZNiV7B9nl0XB7To9xNjCwWILDqs2qkp5qDQRPm1Wl0xWbENrXBD3w7yDnZdt7/Ywp3WGj0+kNjMWOt4YWF/k5qVGtbSiSgCyEEEKIpBjSAdntw+Xx1xNnOKxgEDJlzmpRSTsVPP3/rAQm7w2Wx6v12Ze4ocVFQR8dLEwZDht6t7HY8TaceyCDBGQhhBBCJElfAbm5uRk9DuObo+EfEnJ6BxkFLJbggGyzqqTZLXQ4fRiGgdWq0uX2///B8vj0PgfbNbS4KMiNMiCfGhbSnqAyi+HcAxkkIAshhBAiSXqrQc7Ly0PXddra2hK9LAzDwO3VA7ut6Q4boITsIKdYVRyp/h7JLo+GqigYOjHpZNHl8mHtpTzB5fbR6fSF9ED2eLWw98/P9tcCN546PBdPnU4vTpdv2LZ4AwnIQgghhEiShoYGMjMzSUkJPWRmTtNLRi9kc+xy56mAHKnEwmZTSUv11yUHDuoRo4Ds9GLtpYNFQ6sbCO0S0eXSwnarKMx1AFDfHP+A/MmxVgBKR6XH/V7xIgFZCCGEEEkRbkiIKZnjpn2nyhs6urxYLQr2FAsoBlZLcGyyWVVS7afGTZ8qXTAUA4938AHZ6dawWXo5oHdqJzj8FD0jpMwjK92G3aZystk56LX1ZeP2E+RkpjBlXE7c7xUvEpCFEEKIM8hf/vIXDh06lOxlAL0HZLPsIhkB2avpYBh0OL2kO2wYgKooIR0ZrBaVNPvpcdOc+pxrkK3eNN3A7dWwWHrZQY7QZ1hRIC3VGrIGRVEozHXEfQe5qc1NxaFmLjq3aNh2sAAJyEIIIcQZ4/XXX+fWW2/lscceS/ZSAH/5RLj6Yxg6O8gZp6bo2cKUO1gsCmmOUzvIp8oabBY1EJYHyuPVUJTew2Vjqwt7ioX0U/c3GfjHO4frx1yYm0pDS3x3kDftPIEBzD93dFzvE28SkIUQQogzQFdXF7fffjtwuntEsg3VEguXR8OiKjS1ucnNSvWPmQ4zsMOiqmQ4/N0hAq3erGqg+8VAeX3+HezemF0iQoK0ATlZ/gN5PcssCnMdNLS40PX49ELWDYMPdtYxdXzOsO5gARKQhRBCiDPCz372Mw4fPkx2dnZSDr6F01tAzs3NBZITkJ1uH1ZVpbHVTX62HS3SDrKqYLdbUBSCeyG7tUG1enN7tOhavPWoPzYMA0WF1BQrWRkpIbvIhbmp+DSD5nb3gNfWm/1HWmhqdXPxecN79xgkIAshhBAj3o4dO3jooYdYunQpc+fOTeoADpPH46G9vT1iQLbZbGRmZiZlrW63htur4XT5yM85tYMcJiCrqoKKv+a3o8scN+1PttF2sqhrdOLTgj/rdPkDeiSGYdDY6iI/J3iMc/d1FuSk4vL2DMhmJ4v4lFls3HGC9FQr550VvmxmOJGALIQQQoxgmqbxjW98g/z8fB544AHy8vKGxA5ybz2QTcmapuf0arR1egBO7yCHK7GwKKD4p9QFj5g2/IM++uDxajS2ukJqljvdvrA71qb2Li8erx7SwcK/0+1fZ1Z6Ssgkvni2euvo8rJ9fyPzZozqde3DxfD/DoQQQggR0fPPP88///lPfvWrX5GXl0d+fv6Q2EHubYqeKRlhXtMNfD6d5jYzIKeiGwYptvAlFqCQ7rAF2ryZomn15nRruNw+Wto9wa+7fL32QG6M0MFC67aDnGKzkJEeXGaRk5mC1aLEpdXb1j0n0XSDi0ZAeQVIQBZCCCFGtIqKCiwWC7feeitwelc2WSOcTdEE5GSEeZ9PR8HfJQLMPsOhU/TAX05hsSikO6yBQSHgb/XmjuKgXnuXB3uKhcZWV6BmWdN0fJoe9n6mBnNtPQKyrhvYugX5wpzUoHZvahxbvVUcamZ0voPSwuE7HKQ7CchCCCHECFZXV0dhYSHqqZrW/Pz8pI1w7i7aHeREB2Sv5m/x1tjqwm5TSXdYUQidomeyWVR/DXK3MgmbVQ0KzJE0t7lJTbHg8+k43f4g6/HqIaURPTVEGBKi9ei2kZWegh7SySI15jXImqZTWdXGWWNzYnrdZJKALIQQQoxgJ0+epKioKPDPyRzh3N1QrUH2niqNaGx1k5ftb6NmQMShHf5x08E7yFaLitPTe0D2+nScZq2xAu2nap49Pr2vfExji5vMNBupKcF10T1LQVJsakgbOHMHeTBdNno6dqIDt0fjrHHZMbtmsklAFkIIIUawuro6Ro0aFfjnZE6o687cQY4mIMcyzPWlqc2FzaL6u0SYO7SG0ecOsten4znVNcJiUfD0MU3P6faB4b+mI8UaKOnweH2ofQwJaWh1hZRX+NcZXApiloB073tcmJuK16fT2uEJ/foB+uRYKwCTx0pAFkIIIcQwUFdXNyR3kBsaGsjKyiIlJSXiZ/Ly8tA0jfb29oSsSdN0mlrdOFKtgR7IACinOlaEkWL1B2QgcFBPVRQ03X+9SDo6PZid3OwpFjq6vHh9Ol0uDWuEe5kaw/RANll77HTbLCpa94CcE/tOFp8cbaW4IM3fOWOEkIAshBBCjFCGYYSUWAylHeTe6o8h8dP0Opw+DMPA7T7dA9kv/CE98NcbZ6T5p+l1H8ChKKfqmSNobvcEl0goCp1OL53O3lu86bpBU5s7/A6yElorbbOpITvIELteyJqmc7CqlbNG0O4xSEAWQgghRqyOjg6cTmdQicVQ2UE+efIkhYWFvX4m0QG5qdWFzarS2OYPumYPZKtFCR3pfIrNZiE3079z2n1X1sDA5wtfGuLT9JAgbLMoNLe5/VP8ItQ7g//woK4bFPQYEmLqudNtswQH5LzsVFRV4WSMdpCP1nbg9uojqv4YJCALIYQQI9bJkycBgnaQkznCubuqqirKysp6/Uwiw7ymGzS2unHYrUEt3vQIY6ZNFlUhL9uOooSWLUSapud0+TAwgkK3eV9d11F7afG2bb//WZSPCRNIjdAd5BSrGtTJwqIqFGTHrpPF/mMtwMiqPwYJyEIIIcSIVVdXBwQHZKvVSnZ2dtJ3kKMJyIksB+l0etFOhdPGbm3UtAhjpk0Wi4JFVcnNsgeFTgUFry/8Qb0Opy8kyKqqgq6DQeRwbBgGm3aeYGJpFqPz00LeU9TQbhs2W3ANMsS21duBo62UFKaRmTZy6o9BArIQQggxYpkBuXuJBSRnAEd3ra2ttLe3R72DnIi1NrX6u1f4/7870AO55/CNniyqf9x0YU4q9S2uoNdd7vABubndjT3M6Gr/ob3IHTsOVbdzotHJxecVhbyn60Zg/d3ZrJYwvZBj0+rNp+kcrB5Z/Y9NEpCFEEKIESpciQUkZ4Rzd1VVVQCMGTOm188lqhxEP1VeYXajaGx1BXog6z2Gb/Sknho3XZjroKHbrqzVogRNsTNpmk5nlzfs6OrMNBvZvXSC+GDnCew2lVlTQw839hwSYgp3uLAw1z9hr73HeOz+OlrbjmcE1h+DBGQhhBBixDJ3kHsehkv2DrIZkPvaQbbb7aSnp8d9rZ1OL5p2uvbX3wPZfwiuzxILVQEDCnJS6XD6/P2N8Zc6hAvITrcWUn9sUpTIhwFdHo2P9jYwa1ohqXZryPuR1mmxKCg9yjZG5cam1dsnR0/1Pw5XDz3MSUAWQgghRqi6ujpyc3NDeg0PlR3kvgIyJGaaXlObO6hzhDlFD8AwwGqNvINs1vwWngqdDadCp9Wi4PZqIWUMrj4m7EXy8b563B4tbHkFELEUJNIOsn+tg6tD3n+sldJR6YE2dyOJBGQhhBBihOrZA9k0FHaQFUWhuLi4z88mIiC3tLtJtftDsNPlo8vlOz1FL0xv4e4sqoKiEmi7Vt/iD52KomAYRsgBuU6nL2ytcF8+2FHHqDwHE0uzwr7vL7EIv4Pcs9I4PycVRYGTgwjImm5wqKptxPU/NklAFkIIIUaonlP0THl5ebS0tKBpvY9Djpfjx49TVFTU6xQ9U7wDsk/TcXu1wA5y9x7IfkbEKXomm0UlP+tUQA4qW1BCWr31NQgknLrGLg5WtXHRuUURSzB0I1INshpy7s9qUcnOSKG5beDjpk82OfH6dMaNzhjwNYYyCchCCCHECHXy5MmQDhbg30E2DIOWlpbELwr/DnJfB/RM+fn5cS0Hcbm1oADZvQcyELa3cE82q4rNZiEjzUZDt04WGODrFpANw8Dp8mLtZ0DeuuckigIXzghfXuG/ePhpf6qqgBLarSI3005Tt8l/ES9rGGG7XdTUdwJQUpje5zWGIwnIQgghxAjV2w4yJG+aXjQ9kE3x3kF2un10P8MW6IF8qmRCUSKPmTbZrCq6carVW/eyBcXAq50Ol16fjqaDGmEXOJL9R1sZV5xJdkbvO+7h1mlRFVRFCWn1lptlp7mt74D8tzcP8eAzO0Ner6nvRFFgdEFamK8a/iQgCyGEECOQ2+2mpaUlYg0yJG+a3kAC8mB79kbS3uUhpdshvKZWNyk2lQyH/+CZgREyfKMnm9XfDq4wN7gXsqooeL2ny1g8Xj3sbm5vPF6No7XtfXeKUIg4otpmVTH08AG5t+fq8mh8sOMEh6rb6OjREq6mvotRuY5+l4sMFyPzuxJCCCHOcPX19UDokBBI7g5ye3s7ra2t/QrIXq+Xzs7O+KynM7gnsb/Fm78Hsr91mqXPHWSr1YKuGxTkOGhucwfqji1qcC9kj1cDo3+7x4dr2vFpRlSjnCPVStuslpDDgrmZdrw+nU5n5K4aH++rx+31fy8Hq9uC3quu76SkcGTuHoMEZCGEEGJECjdm2pTMHeT+tHiD+Ib5ngf0ILgHss+n47BHbvFmSrH6xzkX5qZiGKfrmC0WFZf7dADtdPmw9nHgr6cDx1pRgEll4btXBPRSK22zqughO8j+co3mXuqQN+2soyAnFYuqcKjqdED2eDUaml0jtv4YJCALIYQQMfPOO+8Eptclm7mOobaDHO0UPZP5uaNHj8Z8LT0P6EFwD2SfpuMIM5SjJ5tVxcDo1l/4dC/k7jvIHU5vv0sSDhxvpawoPTDlLxzDMFBUIpaC2CxK2B1kgJYIdcgnm5xUHm/j4vNGM3Z0Bge7BeTahi4MRu4BPYhzQB4/fjwzZsxg5syZzJkzB/D/tLpo0SImT57MokWLaG5uBvy/uHfccQfl5eWce+65fPzxx/FcmhBCCBFTe/fuZcGCBTzyyCPJXgrQ+w5yTk4OiqIMix3kyZMnA3DgwIGYr6XnAb2ePZB9moEjte8dZItFAUOhIOfUhLpTvZAtqoLbqwfqfLv62eLN69M5XN3eZ3mFrhu99la22dSwh/SAiJ0sNu+qO9U5YxSTyrI4WtseKB2pqe8CkBKLwXjrrbfYvn07H374IQDLly/niiuu4MCBA1xxxRUsX74cgFdffZUDBw5w4MABVqxYwe233x7vpQkhhBAx89///d8YhjFkdpB7C8iqqpKbm5vUHeSSkpKoPj9mzBhsNhuVlZUxX0vPA3rHT3YApyfNGRC2t3BPZleKrHQbdpsa6IWsKAoKBj7NwOvT0fXT46yjceyEP5T2dUDPPyQk8jpTbCp6cDtmstJTUFUlbC9kXTfYvPsk0ybkkpNpZ2JZFj7N4PgJ//Opqe/EalECI6tHooSXWKxevZolS5YAsGTJElatWhV4/bbbbkNRFC688EJaWlqora1N9PKEEEKIftu/fz/PP/88kLzOED2dPHmStLQ00tPD/zV4sqbpHT9+nFGjRmG32/v+MGC1Wpk4cWJcdpB7HtDbvPMkqSkWpk/IDbxm62XMtMliUUHxB+KCXEdQL2TjVC9kj1cLmWjXl0+OtQJQHk1A7mVn2mKx0LNZhaoq5GSkhG31tv9oC81tbi461//DlVn/bB7Uq67vpLggrV9hf7jpu7BmEBRF4aqrrkJRFL75zW+ybNky6urqAqMlR48eHfgJt7q6OqgeqaysjOrq6pAxlCtWrGDFihUAnDhxgpqamnh+C8OWeXpZDI48x9iQ5xgb8hxjIx7P8T/+4z+w2+2UlZUNmf82HTlyhIKCgohrycjIoKamZsBrHehzPHjwIEVFRf26b1lZGRUVFTF9rppucPJkM5mn2rm5PBof7j3JzPIsOtsa6MRfM9xQ7+3zYJ3Xq9Pa1ILuspGdpnCioYOmBn++6XD6qKp2oenQ2tyO7rIFfW1rS+QfUioq6xmVm4Knq4mmrsj3d7p9+DLs1FjDd/rocHppbelAdwfHvkyHQn1Te2Ctprf/WYvDrlKW6wu8l5dlY+/Bk8yeZKOqrp3y0vSQr+tNu9NLba0vbqE61n+u4xqQ33//fUpLSzl58iSLFi1i6tSpQe8rihJxZGIky5YtY9myZQDMmTMn6r+iORPJs4kNeY6xIc8xNuQ5xkYsn+Mnn3zCqlWruOuuuzhw4ABHjx4dEr9O7e3tlJSURFxLcXExdXV1g1rrQL62vr6eiRMn9utrZ8yYwaZNmyguLu53boiko8tLdouVnFOH1d7bVovXZ7Bw3gTyCjLRDQNbl5cxZYV93tOn6ZzssJGdYaekqJNPjteQkz8KVVFQ2z3kF2ThdPvI8znITAsd9pFXEFoGo+kGx09WMm9GUdj3u2vr9FAyKp3igvB/W9DR5aXF1URORvCufWFeM0dq24Ou73L72Hu0kovOLaJo9OlNysljW9hzqBl7eh7tXZ8wviy/z3V1p3a4KS4u7LOn9GDE8s9dXEssSktLAf8J2htuuIGtW7dSVFQUKJ2ora0NnK4tLS3l+PHjga+tqqoKfL0QQggxVP3P//wPdrud73//+3Gf+tYfdXV1YTtYmPLy8pJWgxztAT1TeXk5XV1dMS297HlAb+OOE5QWpjGuOAMATTNItVujCuSqogRKGApzHPg0g9Z2f22vqoLb6+83HE25hun4iQ7cXp2zouh/rBu91yBbVCWkWwf4D+q1tAcPCzle14nXp3NOeV7QZyeVZdHR5WXHJ/7fMyP5gB7EMSB3dnbS3t4e+P9vvPEG55xzDosXL2blypUArFy5kuuvvx6AxYsX8/TTT2MYBps3byY7OzukvEIIIYQYSiorK3n22Wf513/9V4qKisjNzQ10Z0q2SGOmTcmoQe7s7KS5ubnfAdnsZBHLg3rdD+hVnezkaG0HF503OhCIfT6d1Ch6IIO/ntdiUQPT9KBHJwu3r98t3qKtPwbA6H0ctsWiQJign5uVgk8zaO82Ja/6pL9Mo2xU8G60WYf87jb/DymlI7jFG8SxxKKuro4bbrgBAJ/Px7/8y79w9dVXM3fuXG6++WaeeOIJxo0bx1//+lcArrnmGtauXUt5eTlpaWk8+eST8VqaEEIIERN//etf0TSN73//+4B/V7ajowOv14vNZuvjq+NH13Xq6+t7Dch5eXm0tbUldK39bfFm6t7q7bLLLovJWrof0PtgxwmsFoV555zecfdqOmlR9EA22az+VmoFOad7IZ811j/+ucPlw6fpfU7k667yeCuj8hxkZ4SWZITTa0BWFcK9a/ZCbm5zk5Xuv091fSfpDmvIfUcXpOFItXK0tgOH3UJOZnTrGq7iFpAnTpzIjh07Ql7Pz89n/fr1Ia8risJjjz0Wr+UIIYQQMXf48GFGjRoV+BvP3Fx/94Pm5uZeyxvirbGxEV3Xe12DOU0vkWsdaECOdas3c4Kew27F69PZsvskM88qIN1x+gcF3TCw9zMga5pOXnYqqgL1LaeHhXS5NQgbUcPTDYPK463MmloY3RcoBE0D7ElVFQxODRTptpNs9kJubnczrjgT8O+ml45KDyktURWFiSWZ7DnUTElh6PsjjUzSE0IIIQbo2LFjjB07NvDP5oS6ZNch99YD2WQG5ETWIfd3ip4p1q3euk+3276/gS6Xj4tnhj6r3lqn9WSz+MdNW1SF7Ex7oH2axeIPzmGLgCNobHHhdGuML8mI7gsMw19GEYGiKFgtCj2G6Z0OyKfWqhsGNfWdEcsnzDKLkV5/DBKQhRBCiAE7evQo48aNC/yzGZCTXYdsDivpq8QCEhvmzYA8kEP45eXlMQvIHu/pqRm7KpvISrdx1ric4A8Z9Ktm2Gbz1yAD5Gam0NJxegCHbpweJhKN2oZTk+oidKUI1XsNMpwqAemRkDPSbFgtp4eFNDS78Hj1kPpj0+mAPLLrj0ECshBCCDEghmFw7NixoIBsllgMlR3kaEosErmDfPz4cQoKCkhNTe33106ePJnKysqgjgsD5XR5A4HyYFUbk8dmhwZYpX8BOcVmQTsVQHMy7bR0G8ChaeG7TLz9UQ1rNoZOXjRHORcX9L1Tq+kGVqvSZ/u0FKsaWJ9JVRRyMu00nxo3XV3vP6BXGiEgl4/J5vMLJzB3epSlH8OYBGQhhBBiAOrr63E6nUOyxGIo7yD3t/7YFMtWb06Xhs2i0tTmpqnNHdgZNWmajt1m6ddQC3uKBd0wd5CD26cV5qaSmhIckDVNZ+37x9i6twWPVwt6r7ahk9wsO47UvmugfT49qs/ZbCpGzxoL/GUWZolF9clOFCVyMFdVhSvnlQXVao9UEpCFEEKIATh27BhA2B3kZJdY1NXVYbVaA+sJJ1k1yAMNyLFs9dbp8mK1qhys8rdSm1QW3ErNqxk4+nFAD/w7tObmdk5WCm6vjtOtRfz8nkPNtHd50Q04eqIj6L2a+q6odo8BvD6d9CjWarNaQnaQwV8OEthBPtnJqFxHrz2VzxQSkIUQQogBOHr0KBAckHNycoDk7yCbQ0J66zSQmZmJ1WpN+A5yfw/ombq3ehsMTTdwezWsFpWDx9uwp1hCSgp8Pj1kx7cvVqsSKNMItE9rd0f8/KZddaSd2vk9XN0WtL4TjV1RH4Tz6dHtIFstamCHuzv/sBAPum5QfaqDhZCALIQQQgxIuIBssVjIyclJ+g7yyZMney2vAH9ng0RO03M6nTQ2Ng54BzlWrd68Pg2MU/XH1W1MKMkMOeAWbejszma1YGDWIPt7BHevQ+6uo8vLrgNNzD+3iPwsG4eq2gPv1Tc78WlG1Af0FBTsUYT5FFuEgJxpR9cN6pud1Le4JCCfIgFZCCGEGIBjx46Rnp4eUsaQm5s7ZHaQ+5LI0dhmsB1IBwuIXas3j1dHUcDp8lF9sjOk/hgAg36XGVhUBeupVm85gR1kT9jP/rPiJJpucOGMIsYWOThU3RaoVw50sIhyB9kgusOE/nHT4abp+de655D/hzoJyH4SkIUQQogBMFu89SxjSGTojOTYsWNRlTLk5+cnbAf5Jz/5CWlpaVxxxRUDvkZ/W71Vn+w41YP4NLdHw8DgcE07hgHlY8IEZPrXA9nkSLXi8+lkZ6SgAC0RSiw27TrJmKJ0ykalM2ZUKu1dXhpODRapqe9EAUbnRxmQjfAdMnqyqEq4adOBgLz7oP/3bNkZ0MItGhKQhRBCDHkdHR2sX7+e1tbWZC8loGcPZFNubm5SSyw6Ozupq6tj0qRJfX42UWH+H//4B6tWreInP/nJgEssoP+t3lo7PHS6fEGvdbl82Cz+A3qqAuNLwgRkxd/1ob/S7FZ8mo7VopKZbqMlzA5y9clOjp/o4MIZ/hKYcUUOAA5V+8ssahu6yM9Jjapswuy2Ec0Ia4tFDTurxKyXPnCsldQUC3nZ9j6vdSaQgCyEEGJIamtr41e/+hWLFi0iPz+fK6+8koceeijZywroOUXPlOwd5EOHDgEwceLEPj+biB3krq4u/u3f/o1p06Zx1113Depa/W315vXptPYIqV1uH1aLSmVVG2VFGSGH8XTdwKKqvY5ujiQ1xYJPO93qLdwhvc276rCoChec7S+BGZWbQmqKhUOnDurV1Ed/QM/fbSO6UhCLqoSddp3usGKzqvg0I+yI6TOVBGQhhBBD0qOPPsp3v/tdampquOOOOxg1alQg/CVbZ2cnjY2NQ3IHuT8BORFh/r777uPIkSM8/vjjpKSkDOpa/W31ZhjQ1OYKeq3L6UNV4EhNe9j6Y5+m97vFm8meYunW6s0eUmKh6QZb95xkRnkeGWn+XsKqqjC+JJND1W34NJ26JifFUR7Q8/l00qLsSezv6Ry6hawoSmAXOdKI6TORBGQhhBBD0p49e5gwYQJ79uzhl7/8JWeddVZgVHGyhetgYTJDZywmvg1EfwJyYWEhnZ2ddHV1xWUt+/fv54EHHuArX/kKl19++aCvN5BWb10uHy6Pv8zCp+nouk51fRcerx4hIBuk2gcWj6xWFZTT46Z7HtKrPtlJW6eXWVMLgl6fWJpJ9clOjp3oQNeNfrV4S4uy24ZFVYj0WzI3y/+DixzQO00CshBCiCFp3759TJ06NfDPY8aMGTYB2efz0dHREfJeIhw6dIjs7Oxeh4SYzIN85tCTWHvsscewWq388pe/jMn1xo4di9Vq7fffJHR2eQF/BwsDhYNV/nKGcAHZ49PIcAxsp9t/sM9fopCTacfp8uHynB4WUnNqlPOY0RlBXzexNAvD8JdfAJREOSREQYl6HLaq+sdRR+qFDBKQu5OALIQQYsjRdZ39+/cHBeSysjKqqqqStjPbnRkow9UgJ3ua3qFDh5g4cWJUtaTm+uMVkHfv3s25557bZ0/maFksFoqKiqipqYn6a+w2S2An1+PVwICDVa0U5KQG2rGZNN1AVdQBH1SzWlQwDAzjdKu37mUW1fWdWC0KhbmOoK8bX5IJwD/31KMqUBRlBwvw9zeOls2qooeZpleQk4qqRN9a7kwgAVkIIcSQU1VVRVdXV0hAdrvdNDQ0JHFlfkePHsVqtVJSUhLyXl5eHpC8aXoHDx6MqoMFnN4BN3fEY62iooKzzz47ptcsLi6O+pAe+OuCW9rd6LqB062hKAYHq9rC7h63d3kpLUwb0AE98O/S2lP8I51zzWEh3QJyTX0XowvSQrpOpDtsFBek4fJoFOY6ot4VNjCwWaPv12y1hA/IC+aU8u9fPm/AtdcjkQRkIYQQQ86+ffsAQgIyMCTKLI4ePUpZWRkWS2g4SeYOsq7rHD58OKr6Y4CSkhIsFktcAnJjYyN1dXVMnz49ptftb0BWFTB0f/cKp9tLl8tHW6eX8cWZQZ/z7x5DQY/d3f5y2P29kE/vIJ+uQ66p74x4EG5CqX890e7iappOijW6Fm+mFKt/kElPaalWJoYbmHIGk4AshBBiyBnqATlSizdI7g5yTU0NHo8n6oBstVopLS2NS4lFRUUFQNIDMgAqtHd66XT6qG/2d7UoGRUcRDudXkYXpEW9exuJ2erNHDdttnrrdHppafdEDMATS/0BNdoOFl5f9Af0TDZb+B1kEUoCshBCiCFn37595ObmUlhYGHjNPFB2/PjxZC0rINKQEEjuDnJ/OliYxo0bF5cdZDMgx6PEor6+Hq/X2+vn1m0+zrot/h+mHCkWmlpdON0aJ5ucAJR0C6K6bqAbBqMGuXsMp6bp6TopNgvpDistbf4d5JrACOnwAfissdlYVCX86OswfJqBo58BOdIOsgjVZ0B+9NFHk9rPUQghxJnH7GDR/aDZqFGjsFqtSd9B9nq9VFdXRwzIydxBHkoBec+ePWRkZEQ18ro/iouLAairq+v1c7sqm3jnoxr0U6OYO13+QF3b0EVmmi3Qhxig3emlOD89qpHNffFfw//7tvuwkJqT/g4WkUosCnMdPHDnhUyf2Hf3EfAH5PR+BmS73Rqx1ZsI1mdArqurY+7cudx888289tprQ+L0sBBCiJGtZ4s38HcwKCkpSXpArqmpQdf1iAE5LS2NlJSUpATkgwcPYrFYIpZ/hDN27Fiqqqrw+Xx9f7gfKioqmDZtWswns5kBua8yi+kT84J2jP3xxaCmIXRSna4bFOalxmR93Us0cjJTAof0auq7cNgtgdKLcPpdMtHPchCbJXIvZBGszyf73//93xw4cIClS5fy1FNPMXnyZP7jP/6DgwcPJmJ9QgghzjCtra3U1taGBGQ43eotmczd1kghVFGUpE3TO3ToEGPHjsVmi266Gvh3kDVN639dbx8qKipiXn8M0Qfks0/txB6qbgf8tcEWVaG2oSuoztfr00lNsZKaEpsODjarinJqYl1Opj3QYq66vpOSwtiNclYU+r3jbbOpyCTp6ET1o4eiKIwePZrRo0djtVppbm7mpptu4u677473+oQQQpxh9u/fDzDkA3KkHWRIzAjncMweyP0Rj1Zvzc3N1NbWxrz+GKIPyGVF6aSlWjlc7R8K4rBb8Xh13B4taAfZ6faRnzOwvsfhWC0KKAqGYZCbaaejy4vXp4fduR4Mf4u3/u4gq7KDHKU+n+wjjzzC7Nmzufvuu7n44ovZtWsXv/nNb/joo4/4+9//nog1CiGEOIOE62BhGjNmDMePH09quV9fO8hAUneQ+xuQze8jlgE5Xh0sAIqKilAUpc+ArCgK44ozOFzTHnittt48KHc6qGq6QXb6wCbnRbpvoJPFqRHOR2rbcbp8EeuP+8un6dhtFtR+tHgDsFhUrFYl4Z0sDMNAUZR+rzeZ+vz7hKamJl588cWQn5RVVeUf//hH3BYmhBDizLRv3z5sNhsTJkwIea+srAyXy0VTUxP5+flJWJ2/xduoUaNwOCJ3PMjLy6O6ujqBq4KOjg5Onjw54IAcy1Zv8QzINpuNgoKCqEpCxpdksvdwC51OL+kOGzUN/oNyZomFYRioitLv2t++OFKsdLq85J7qhVxxyP/DUqQOFv3l8+mkp0ZfRtNdaooVn6aTog7+QGK0fJqBI8US83r0eOpzB/lnP/tZxL9GmjZtWswXJIQQ4sy2b98+ysvLw9bRDoVeyEePHu3zEFwySiwG0sECID09nYKCgpjuIO/Zs4e0tLRey1AGI9peyOYwEHMXuaa+i+yMlEAgdns0sjJSsAxwcl4kjlQLPu30sJA9B/2/F2JVYuH26aQ7Bhbq0+wWvD49JuuIltenkzbA9SaL9EEWQggxpITrYGEaCgH5+PHjfQbkZJRYDDQgg38XOdYlFtOmTUNV4xMzog3IY4oyUBU4VOWvQ+5ZB+zy6ORlxa7+2OSwW9F0Ah0rjtd1kp2RQrpjYLu+3em6gWEY5OcMrOuG3W5NeC9k7yB2vJNFArIQQoghw+v1UllZyZQpU8K+b/bUTWZArqmpoaSkpNfP5OXl0dbWFvPWab0xA/KkSZP6/bWx7oUcrw4WpnABubGxkfPOOy+o/NOeYqGkMJ1DNe3ohsGJhq6gASFAUD/kWLFaVRT8QTk1xV/KEKv64/YuL2WFA+/ZnJpiQU9wDb8BpNoTV9IRCxKQhRBCDBmHDx/G6/VG3EEePXo0FosladP0nE4nra2tgU4KkZjT9FpaWhKwKr9Dhw6Rk5MTuHd/jBs3jmPHjsXk8GNrayvV1dVx6WBhKi4upq6uDl0/XSqwd+9edu7cicUSHMQmlmZypKad+iYnXp9O8akdZE3TsVqVQICNJZtVDTxLcxe552jrgfBpOqqqMCp/4Nca7CjtATEMUqwSkIUQQogB6a2DBfiHhRQXFydtB/nEiROAP6j3JhnT9AbSwcI0duxYOjs7Y7LeeB7QMxUXF+Pz+WhoaAi8tnfvXiD0986EkkzcHo0P99YDUFLgD5dOt0Z+dmpcDo5ZLaf7DZsH9XruXA9Ee5ePMUUZWAdRM22zqpDwJjAKKbbhFTmH12qFEEKMaGZAjlRiAcnthWz+tX60O8iJDMgHDx4ccECOZS/kRAVkCO6FvHfvXhwOR9DBQEWB8aVZAHywwz+auvhUQPYfootde7furBYFA38v5JxTNc6lowYXkD1ejdQUdcC1x4G1WVVzEnZCaLqB1arE/CBkvA2v1QohhBjR9u3bx+jRo8nJyYn4mWQGZHMHua+AbO4gJ+qgnqZpHDlyZNABORat3ioqKnA4HIwfP37Q14okUkCeMmVK0MHAVLuV7HQbWek2mtrc5GXZSbVb/eUPCqTF6eCYoijYbSq6bjAq14HNqjI6P3JbwGh0unyMHZ2BZZC9hC2qQorVgqYlppOF/4De8OpgARKQhRBCDCG9dbAwJXNYiBnIhlqJRU1NDR6PZ1AlFhCbHeQ9e/YwderUkFrgWIoUkHu2n81Ks+HVDCae2kU2O1h4vDoZjpS41uOmnBoWsnBuCT9aOmvAh+rg1C6sRSU7IzYdN+x2/9oSwefTSYtB945Ek4AshBBiyIhmF7SsrIyurq6EHoAz1dbWoqoqhYWFvX7OLLFI1A7ywYMHgYF1sAAoKCjA4XDErMQinuUVEBqQOzs7OXr0aEhATnNY0Q2YcCogmwNC3F6NnIz4hja7TUXTDVJsFkblDW732OnykZ9tj9kkujS7f1hIIvg0Q3aQhRBCiIHy+XzU1dVRWlra6+eS2Qv5xIkTFBUV9bk7muga5I0bNwIwY8aMAX29oiiBThbROHnyZNjvrbm5mePHj8e1gwWAw+EgOzs7EJD3798PhA4ws6dYUIDyMn9ANuuANZ2472o6UqxoemxCqE8zyM0aXO1xd+Yo7IRQjEHtnieLBGQhhBBDgtm2q68ew8kMyLW1tX2WVwBYrVYyMzMTFpBff/11zj//fIqKigZ8jWiHhbhcLubPn8/tt98e8t4HH3wAwPz58we8jmh174VsdrDoGZDN1mLjSzK4/abpzJ5WAPgP79nj0N4t6N4plpgM5NANA0WJbb9me4qFhFUoGQy7DhYgAVkIIc5Ibrc7MFhiqKiurgYY0jvItbW1fR7QM+Xl5SWkxKK1tZUPPviAq6++elDXiXZYyMMPP8yhQ4fYsmULHR0dQe9t3LgRq9XKBRdcMKi1RKNnQLZYLEyePDnoM6qqkO6w4dMMzp2cj9Xi70+sAPY472r6W7ENviTC6fKRl20f9OG87mxWFZT4J2T/YUglOb2XB2n4rVgIIcSgPfLII0yePJm1a9cmeykBNTU1AH3uIBcXF6OqalKGhZw4caJfATkRO8gbNmxA07SYBOT6+nq6uroifqa6upr77ruPCRMm4PV6ee+994Le37hxI7NmzSItbfBDMfrSMyBPmjSJlJTQtm2Z6TY83tOlDl6fTrrDFrN63kisFiUm3dQ8Pp387NiVV4A5LCT+vd58moHDbolLr+l4k4AshBBnoI8++ghd17n55pvZtm1bspcDRL+DbLPZGD16dMJ3kDVNo66uLqoSC/DXISdiB/m1114jMzNz0GUNZieL3n7w+MEPfoDP5+Mf//gHdruddevWBd7zeDxs3bqViy++eFDriJYZkA3DCNvBwpTusOHrVgvs8epkpse/q4K5W90Xry9ynbJ/t1uJ+Thsc9BIvDvB+H8YGX4H9EACshBCnJF2797N/PnzycvL47rrrkva6ObuampqsFgsjBo1qs/PJqMXcn19PbquD6kdZMMweO2117jiiiuw2QYXovoaFrJ582aeeeYZ/v3f/53p06dzwQUX8Oabbwbe//jjj3G5XAkNyC6Xi8bGRg4cOBAxIPtHSZ/ewfTp/h3keLNGsUvb5fLR6fLh8Wph33d5NLIzUwY1OS8cVVUCXTbiyd8Defi1eAMJyEIIccZxu93s37+fhQsX8sorr9De3s61115LW1tbUtdVXV0dKJ/oSzICcrRDQkyx3kF2uVx89atfDUyqA3/3hmPHjg26vAIIDPY4fPhwyHu6rnPnnXdSXFzMD37wAwAuvfRSdu3aFXgu77//PkDCArJZivP+++/j8/kiBmT/YbzTQdAwzNAcXxZVwWJR0CPs0np9Oj5NJzfLHnEX2e3VKMiOTe/jnhx2a9w7WRgGpNqHXwcLSEBA1jSN888/n+uuuw7w/8GbN28e5eXl3HLLLXg8HsD/L+xbbrmF8vJy5s2bx5EjR+K9NCGEOCPt378fTdM455xzmDFjBn//+9/ZvXs3jzzySFLXVVNT02d5hSkZw0KiHRJiMneQY7XGnTt3snLlSm699VZcLhfgL68A+PSnPz3o65eVlZGamsonn3wS8t67777L1q1b+Z//+R8yMjIAf0AGWL9+PeCvP540aVLUz2ewzB9UNmzYAIR2sDBZVAWH3YrXp/tLFhLQwcKUYlPRwoRQXTdo7/QweWwO2ekpeH3hf48oQGZ6fMZhp6ZY8PVS3hETCtisEpDDeuSRR4J+095zzz3cddddVFZWkpubyxNPPAHAE088QW5uLpWVldx1113cc8898V6aEEKckXbv3g3AOeecA8CiRYsYP358oFVWslRXV/d5QM80ZswYOjo6aG1tjfOqTjMDcrQ7yCUlJXg8Hurr62N6/127dvHjH/8Y8AfkKVOmxGSss6qqnHXWWYGewt3t2rULgM985jOB18455xzy8vJ48803MQyDjRs3cskllwx6HdHqGZB7m8CY6bDh8WqnDo1ZY9oRojepKdawI51bOjyMK8kiOyMFmy18Rwm3RyM9LSVuPYQdqdag2uz4MIZlizeIc0CuqqrilVde4etf/zrgr5XasGEDN910EwBLlixh1apVAKxevZolS5YAcNNNN7F+/fqkjBEVQoiRbvfu3VitVs4666zAa5MmTaKysjKJq+rfDrJ5oCzawRax0N8dZPP5htuRHcz9Fy9ezEMPPcRrr73GO++8E5PyCtOUKVPYt29fyOv79u0jOzs7qM+yqqpcccUVvPnmmxw4cID6+vqElVfA6YC8Z88eysrKyMzMjPjZjHT/yGmPVyMrTjuy4YSr8+10esnPsTM63z9dz2a1dK8ACXB7NXIz4rdWf3CN3w8K5njsWNdPJ0pcjxZ+5zvf4YEHHqC9vR2AxsZGcnJysFr9ty0rKwucWq6urmbMmDH+RVmtZGdn09jYSEFBQdA1V6xYwYoVKwB/PZjZFkgEi9WOxZlOnmNsyHOMjVg9xw8//JBJkybR0NAQeG306NF8+OGHSft3qtPppKWlhYyMjKjW4HD4w8W2bdtC/jvRl4E+x4MHD5KVlUVzc3NUtcXZ2dkAbNmypc/x2dH45JNPUBSF+++/n507d3LDDTfgcrmYM2dOzH7dSktL+fvf/87hw4ex20/Xvu7YsYOJEycGQjr4n+OcOXP429/+FijPmTx5csJ+DxmGgcPhwOl0MmHChF7v63T5aG1qAxQcajo1RntC1tjW6qKx0Ym7WyeHdqcXhyWT2lp/O73mpga6umx4nWrQznZ7l5cseyaGNz5/S+LyaLQ2t6G74hMF3T6dFKtKTY03LtfvKdb/nYlbQP7HP/7BqFGjmD17Nm+//XbMrrts2TKWLVsGwJw5c6L+67gzkTyb2JDnGBvyHGMjFs+xsrKSCy64IOha5513Hn/6059wOByBMcmJZO5eT5s2Larv0eyr2tnZOaBnMpCvaWtro6SkJOqvLSoqIiUlhZMnT8bk162jo4NRo0YxdepUnnvuOS6++GLsdjs33nhjzPoOz5kzB13X6erqYsKECYHXDx8+zKJFi0K+jy984Qvcc889rFy5kry8PC677LKoDlnGSklJCQcPHmTmzJm9PmOvT6exqx4FhTFjchPSxQLAkeGmS28hJ+P0Dxtqh4exY/JISz0dwdJ96XS6vDjsp19T292MG1MQt3ppj1ejodNOTpx2qds6PRTlp1FSlBGX64cTy//OxO138caNG3n55ZcZP348t956Kxs2bODOO++kpaUFn88H+EswzL9OKy0tDbQZ8vl8tLa2kp+fH6/lCSHEGamjo4PDhw8H6o9NkyZNAvy7pMlg/m1if8KnzWZLeIlFtPXHABaLhfLy8piWWJj3v/DCC3n00Uf54Q9/GNOhHGYdb/c65La2NmpqasLW+E6YMIGJEyfS2dnJRRddlNBwDKfLLCId0DPZrCopNgs6RsIO6IF/WAhGjzIGw1960V16mi2ok4WuG1hUJa71u/7Sh/iUsmq6gWEYFObGdsBJIsXtyf/iF7+gqqqKI0eO8Pzzz7Nw4UKeffZZFixYwAsvvADAypUruf766wF/TdXKlSsBeOGFF1i4cOGwnLwihBBDmdkibKgFZPOvx6OtQVZVlTFjxkQ1GjlWTpw40e8ODWeddVZcAjLAt771rcBhvVgx66a71yGbYTnSIbgrr7wSIKEH9EzRBmSAjDQbqSmWhNbE9gyhXp9Oqt2Cpcca0lKt6N1qlT1ejcz0lLjmIFVVsFri0wu5rdNLWVEmqSnDc0gIJKEP8v3338/DDz9MeXk5jY2NLF26FIClS5fS2NhIeXk5Dz/8MMuXL0/00oQQYsQzO1jMmDEj6PVkB+T+7iCDf7BFonaQDcPo9w4y+ANnZWUlmhZ+EER/DOT+/ZWZmUlpaWnQDrIZliMF5GuuuQaAyy+/PK5rC6c/ATkzzZbQA3oQOizE69PJSAsNjfYUNSgMe3x63Nq7dZeaYgnbZWMwXG4faakWRuU5YnrdREtItL/88ssDf3AmTpzI1q1bQz6TmprK3/72t0QsRwghzli7du3C4XAE1ZcCpKenM3r06KR1sqipqSE9PZ2srKyov2bs2LGBHrzx1tbWhtPpHFBA9ng8HD16dFAH9TRN48SJE3EPyBDayWLfvn1YrdbAD1E9LV68mJ07d4b80JUIn//85/F6vVFNX8zJtMd8ZHNfrBYlqFGEx6eRmZYe8rmUU72C/X2aFXTdCKpRjpcUm4XOLi/E6LEYhkGXW+OcSXkJa6UXL8Oz94YQQogB2b17N2effXbYWtFJkyYldQe5pKSkX3+lPHbsWGpqavB6439K3pwW198SiylTpgCDb/XW3zHXgzF16lT2798faLW6b98+Jk2aFHGUtaIoSQnHAJdddhmPP/54VL9v7CmWhB3OMymKQopVPb1Layhhp/ipqkK6o1sdsqLgSMAEOnuKJaYlFu1dXkbnOxL+g0g8SEAWQogzyO7du0Pqj03l5eVJrUGOtv7YNHbsWHRdT0hbsf4OCTHFqhfyQO8/EFOmTKG1tZWTJ08C/oDc2xAO0Tu7PTiERjokmOGw4vHq/gN6FgWbNf4RLTWGAdkwDHTDoKQwdId8OJKALIQQZ4iGhgZOnDgRMSBPmjSJ6upqnE5nglfWvyl6pkQOCxloQC0sLCQ7O3tYBWQzDO/btw+fz8eBAwckIA+C3abi0ww03cBiVSJOxstIs+HTddxejUyHLSGNCmxWlVjNZHO5NXIy7HGb/JdoEpCFEOIMsWfPHiC0g4XJrDE9dOhQwtYE/p2ngewgjxs3DiAhnSwGWmKhKErE8c39kegdZPB3rzh8+DBer1cC8iDYbRY0Xcd7KvhG/FyKBcVQ8Hh1suI4Qa87qyX8mOuBcHl1CnOH98G87iQgCyHEGcLsYNFbiQUkvpNFU1MTbre73zvI5vTVRO0g2+32AQ1RiUWrt/6OuR6MMWPG4HA42LdvX58dLETfUu1WNN3wd6bopTbXnmLBUAwMg4Qc0AOwWhWUGIyb1g0DVYHM9OFfe2ySgCyEEGeI3bt3k5OTEzGImjvIie5k0d8eyKa0tDQKCgoSFpBHjx49oL/2njJlCseOHRtU6UptbS25ubmkpsZ/8IKqqoFdbzMgm7vKov+sFgVQ0A1Ic0QOvlaLSorVgm4YYQ/yxWdtKkYMhoW43Bq5WfaE9piOt5HznQghhOjV7t27mTFjRsSQl5eXR3Z2dsJ3kAfSA9k0duzYhATkgQwJMZkH9Qbzg0dtbW1Cx7VPnTo1sINcVFSUlPHjI0VgWIhBn/W5GWk2Umxqwup4rRYVVVECHUvC0Q2DpjZX0CCTnjxebVhPzQtHArIQQpwhPvnkk153AhVFSUoni4HuIEPiAvJghnSYAXkwdciJGBLS3ZQpUzhy5Ajbt2+X3eNBslr8B+EUxV+P3JtkDDNJsVnwaZHDb2uHF7vNgscbftiNrvt7N2ckuIVevElAFkKIM0BXVxcnT55k/PjxvX5u0qRJCS+xMHeQBxIAx44dy9GjR3vdAYuFwQTUyZMnA4Nr9ZbogDx16lR0Xefjjz+W+uNBslqUQF2x2sfwjKyMFIoSPIHObos8brqty0NOho1R+Wl4fOEn7jndPgpyUkPGZw93I+u7EUIIEZa5yxpNQD569Cg+ny8Bq/KrqamhoKAAu93e768dN24cHR0dtLa2xmFlfm63m6ampgGXWGRkZFBaWjrggDzQMdeD0X3XWALy4FgsKlaL0usBPZPDbk3IiOnuUu3WsOOmXR4Ni6IyoTSLDIeVSBUWHp9Ofs7IKq8ACchCCHFGOHLkCNB3QC4vL8fn8yWkbME0kB7IJrMXcjxbvdXV1QGDa7E2mE4Wzc3NeDyehAZksywEJCDHgiPVOmSny6XYQoeFaJqOy+3jrHHZpNgs/proMAFZ0w2sFjXhEwoTQQKyEELE2HvvvccjjzyS7GUEiTYgm50sElmHPJAeyKZEDAuJRQ/iwQTkRPZANmVkZFBWVgZIQI6FzDQbDntiWrf1lz3Fgt5jA7nT5aOsKCMQfO02C4pCSCmTy+0jL8uOpY/SkeFIArIQQsTYo48+yne+852E1/L25ujRo9hstj5DVjJavcViB3mwAdnj8fDv//7vVFVVhby3d+9eYGBdNkxnnXUWjY2NNDY29vtrkxGQwV9mkZqaGnjGYuDGjs7EkaDexv1ltSj0bGyj60bQjreqKqSlWvH2qEP2+gyyEzTUJNEkIAshRIyZu68rVqxI8kpOO3LkCGPHjkVVe//XfklJCampqQnbQfb5fNTV1Q14B3nUqFGkpKQMOiBv3bqVhx9+mB/+8IdBrxuGwWOPPcZZZ53FzJkzB3x9s2Qhml3kZ599lpdeeinwz8kKyEuWLOHb3/42FsvIGB0swvP3Qu5BUUJ6MWek2UICMoq/hnkkkoAshBAxZBhGIFz+8Y9/xOVyJXlFfkeOHOmzvAL8QyImTpyYsIB84sQJDMMY8O6sqqoxafVm7pg/++yzQbvn7733Hh9++CF33XVXnz9c9CbagOzz+fi3f/s3/vVf/xWv1wskLyB/5Stf4cEHH0zoPUXiWS3Bs/R03cBiUUJ6MWem2fD6Tkdpf7mFgT1BQ00STQKyEELEUFNTE62trVx77bU0Njby97//PdlLAvwBedy4cVF9Ntat3txuN9/73vc4fPhwyHuD6YFsilVAtlgs2Gw2/ud//ifw+kMPPUR+fj633XbboK4/YcIELBZLn8/1gw8+oLm5mZMnT/LKK68A/meUkZFBRkbGoNYgRDj+Ps2ng6/bq5EV5tBdSooFlNOf8/p0MhwpI7L+GCQgCyFETJk7r9/4xjeYPHkyv/nNb5K8InC5XJw4cSKqHWTwd7I4dOhQzHoLf/TRRzz00ENcffXVNDU1Bb33t7/9DWBQda5mL+TBOHDgABMmTOCb3/wmzzzzDIcOHeKTTz5hzZo1fOtb3yItLW1Q17fZbIwfP54DBw70+rmXX34Zm81GUVERf/zjH4HE90AWZxZVVbBaT/dC9nh1ssLUFdt7dLJwezWyM0Ze9wqTBGQhhIghc4ewvLycb37zm2zcuJFdu3YldU3R9kA2TZo0ia6uLk6cOBGT+x8/fhzwlxd87nOfw+12A3Dffffx4IMP8vWvf50ZM2YM+Ppjx46lpqYmUJIwEJWVlZSXl3P33XdjtVq57777+PWvf43NZuNb3/rWgK/bXXl5eZ87yGvWrGHBggV87WtfY+3atdTW1kpAFnGXmhLcCzncgUKb1T8C2/ycpjNkW9fFggRkIYSIIXMHeeLEiXz1q1/Fbrfzu9/9LqlrirbFmynWnSzMgPy73/2O9957j6997Wv86le/4oc//CFf+tKX+O1vf4vS8xh9P4wdOxbDMAIT+frLMAwOHDjA5MmTKSkpYdmyZaxcuZInn3ySL3/5ywMeENKTGZAj7cx/8sknfPLJJ3z2s5/l//v//j80TWPlypUSkEXcpdhUtFPjpg2DkAN6pgyH7fREPcMgNWVkHtADCchCCBFTBw8epLS0FIfDQX5+PjfffDNPP/00HR0dSVuTGZCjrUEuLy8HYtcL+dixY2RmZrJs2TJ+8Ytf8Nxzz/Hd736Xz3/+8zz11FOD7pJgfl8DrUOur6+nvb098H3fc889qKqKy+XirrvuGtTauisvL6e1tZWGhoaw769ZswaAz372s0yePJnLLruMP/7xjxKQRdw57P5hIZpuYLWGHtAzpZ/qZOE/yKeSYhu5MXLkfmdCCJEEBw8eDOzAAvzrv/4r7e3trFq1KmlrOnr0KFarNepOEePGjcNiscQsIB8/fjxQY3zPPfdw7733smTJEv785z9jtQ5+B8q8tvmDQH+ZdcFmQC4tLeVnP/sZ/+///T/OOeecQa/PNHnyZCDyzvzLL7/MueeeGwj8S5cu5cCBA3R2dkpAFnGVYrPg03U8Xo2stMh9jdNSrWi64a8/Tk8Z1N/8DHUSkIUQIoZ6BuQLL7yQjIwMtmzZkrQ1HTlyhDFjxkQdRm02G2PHjo1picWYMWMAUBSFX/ziFzz11FOkpMRmwMCECROw2Wzs27dvQF9vfp9mgAW49957efTRR2OyPpMZwMM916amJjZu3MhnP/vZwGuf//znyczMBBLf4k2cWWxWFQwFj08nq5eDd/YUFVXp+3MjgQRkIYSIka6uLmpra4MCsqqqnH/++Xz00UdJW1e0PZC7Ky8vj+kOshmQ48Fms3HWWWexZ8+eAX39gQMHsFgs/X5G/TVhwgRUVQ0bkF999VU0TWPx4sWB19LT0/niF78IDG6KnxB9sVpVFMXA0Ol1JHaK1V96oesGaakSkIUQQkTh0KFDAEEBGWD27Nls374dn8+XjGVx9OjRqOuPTZMmTYpJQHa5XJw8eTKuARlg+vTpVFRU9LmWRx55hLfeeivo9crKSsaPH4/NFt//4KekpDBu3Liwrd7WrFnD6NGjmTNnTtDr3/nOd7jkkksGNcVPiL5YLQqg0NfgD1VVSHfY0A2DVPvIHBBikoAshBAxYgbKngF51qxZOJ3OAZcADIbb7aampqbfu6OTJk2iqamJ5ubmQd2/qqoKICEB+dChQzidzpD3DMPghRdeYNq0aXznO9/hRz/6UVAniQMHDgTKH+ItXKs3j8fDq6++yrXXXhsyrW/atGm899575OfnJ2R94sxks6houh5o5dabDIeVNLsNq2VkR8iR/d0JIUQCRQrIs2fPBuDjjz9O+JqOHz+OYRgDKrGAwXeyMFu8JSIg67rO/v37g15va2tj4cKFfOELXwh00jhy5Ai7d+8G/OG5srIyqP44nsIF5I0bN9LW1hZUfyxEIlksKlaLSmZ63+cCMtJs5GbZE7Cq5JKALIQQMVJZWUlOTg55eXlBr0+ZMoX09PSk1CH3t8WbyQz5sQrIg5mUF42zzz4bIKTM4sUXX+Ttt9/m4Ycf5uOPP+ZnP/sZiqLw0ksvAf4Wb21tbQndQW5ubqaxsTHw2ltvvYWqqixYsCAhaxAiHHuKhaz0vsuMsjPtlBQObrLkcCABWQghYqRnBwuTxWJh5syZSQnI5gjm/u4gT5w4ERj8sBAzIJeVlQ3qOn2ZPHkyFoslJCBv2bKF7Oxs7rzzTqxWK6NHj2b27NmBgByug0W819n9vgDvvPMOs2fPJisrKyFrECKczDRbVAfvLKoy4ssrQAKyEELETKSADP4yi23btqFpWkLXdOTIESwWS78Danp6OsXFxTHZQS4oKMDhcAzqOn1JSUlh8uTJIZ0sNm/ezNy5c4Nqez/zmc+wfft2jhw5EtIDOd56tnpzOp1s3ryZT33qUwm5vxCRlBVlkO4YuZPx+ksCshBCxIDP5+Po0aMRA/KsWbPo6urik08+Sei6jhw5QllZ2YAGcsSik8WxY8fiXn9sOvvss4N2kDs7O9m1axcXXnhh0Oc+/elPA7Bq1SoqKysT0uLNNGHCBBRFCQTzzZs34/F4uPzyyxNyfyEisVrUET34o78kIAshRAwcO3YMn88XcSfSPKiX6DKLI0eO9Lv+2DRp0qSYlFgkKiBPnz6dyspK3G434H/WmqYxb968oM9NmDCBc845h5deeonKykrGjRsXs6ElfUlNTWXMmDGB5/rOO++gqiqXXHJJQu4vhIiOBGQhhIiBSB0sTFOnTsXhcCQ8IB89enTAu6OTJk2ipqYmbOu0aHUfMx1vPTtZmNMLewZkgBtuuIH333+fLVu2JKz+2DR58uRAQH777bc5//zzyc7OTugahBC9k4AshBAx0FdAtlqtCT+o5/F4qK6uHnBANnfDzQEo/dXe3k5ra2tCd5DhdCeLzZs3M3HiRAoLC0M+e8MNN6DrOocPH05Y/bHJbPXmcrnYvHmzlFcIMQRJQBZCiBg4ePAgdru915HAs2bNYtu2bei6npA1VVVVoev6oEosYOCdLBLVA9k0ZcoUVFUNBOQtW7aE1B+bZs6cGXguid5BLi8vp7Gxkddeew232y0BWYghSAKyEELEwMGDB5k4cWLIJLTuZs+eTUdHR9hRw4PRfSpcd3v37gX63+LNNNheyMeOHQMSF5Dtdjvl5eXs2bOHqqoqqqurw5ZXACiKwuc+9zkgcR0sTGYg/8Mf/iD1x0IMURKQhRAiBiorKyOWV5jicVDv2LFjZGRkcMsttwR6HgM8//zzfPGLXyQ/P5+ZM2cO6Np5eXnk5OREHZB1XQ/aHU/0DjL4yywqKioC9ceRdpABli5dyvnnn88FF1yQqOUBpwP5q6++ysyZM8nJyUno/YUQfZOALIQQg2QYBocOHeozIE+fPp3U1NSYBuStW7fS1dXF3//+d6ZOncpPfvITvvnNb/LFL36RGTNm8PHHH4dM9ouWoihRd7Lo7Oxk3rx5fOUrXwm8dvz4cRRF6bXsJNbOPvtsDhw4wLvvvktKSgrnnXdexM+azydcjXI8mUNYdF2X8gohhigJyEIIMUjV1dV0dnYyZcqUXj9ntVo577zzYhqQKyoqUBSFPXv28LnPfY6f//znrFixgnvvvZe333570B0koumFbBgGy5Yt48MPP+TPf/5z4Ps7fvw4JSUl2Gx9T+eKlenTp6NpGs8//zyzZs3Cbrcn7N7RcjgcgcEtEpCFGJokIAshxCCZbcX6CsgA559/Ptu3b49YN9xfe/fuZfz48UyZMoXnnnuOzZs389577/GLX/wiJsG0vLyco0eP4vV6I37mN7/5DX/+85+5++67ycnJ4b/+67+AxPZANpmdLE6ePBmx/ngomDx5MoqicOmllyZ7KUKIMGSmoBBCDNK+ffsAf6/jvpx77rn89re/paqqKibhsaKiIhAKIXzP38GYNGkSPp+PY8eOhS0h2bp1K9/5zne45ppr+MUvfkF6ejo//elP2bZtG8ePH++1xCEezE4Wuq73Wn+cbNdffz2lpaVSfyzEECU7yEIIMUj79+8nIyOD4uLiPj87Y8YMAHbt2jXo+2qaxv79+4MCcqz11smipaWFL3zhC5SUlPDMM8+gqip33HEH2dnZ/PznP0/omGmTw+EI1PgO5R3kO++8k2eeeSbZyxBCRBC3gOxyubjgggs477zzOPvss/npT38KwOHDh5k3bx7l5eXccssteDweANxuN7fccgvl5eXMmzePI0eOxGtpQggRU/v27WPq1KkoitLnZ8855xwgNgH58OHDuN1upk2bNuhrRWKWjZi9hbtbs2YNx44dY+XKlYGDgDk5OXznO9/hpZdewuVyJTwgg/8ZFxUVDbi9nRBCxC0g2+12NmzYwI4dO9i+fTuvvfYamzdv5p577uGuu+6isrKS3NxcnnjiCQCeeOIJcnNzqays5K677uKee+6J19KEEMPY9u3befbZZ5O9jCD79++Pqv4Y/AFyzJgx7Ny5c9D3NUNrPHeQR48eTXFxcdiDhdu2bSM1NZWLL7446PU777yTrKwsgISNme7uwQcf5OWXX47qBxYhhAgnbgFZURQyMjIA8Hq9eL1eFEVhw4YN3HTTTQAsWbKEVatWAbB69WqWLFkCwE033cT69etjdohFCDFy/PznP+fLX/4yL7zwQrKXAvjbmx07diyq+mPTjBkzYrKDbA4C6c+9B2L27Nl8/PHHIa9v27aNc889F6s1+DhLbm4ud955JzDwISWDMWnSpIT3NhZCjCxxPaSnaRqzZ8+msrKSb3/720yaNImcnJzAv0zLysqorq4G/G2SzL+Ks1qtZGdn09jYSEFBQdA1V6xYwYoVKwA4ceIENTU18fwWhq36+vpkL2FEkOcYG7F8jmZQ+9rXvsaoUaMSPgWtp927dwNQWFgY9b+PJk6cyLp16zh69Gi/Ok30fI4ffvgho0ePprOzk87OzugX3U9nnXUWa9eupbKykrS0NMDf2u3jjz9m8eLFYb/vJUuWUFJSQlFR0ZD797T8uY4NeY6xIc8xNmL9HOMakC0WC9u3b6elpYUbbrghcNJ7MJYtW8ayZcsAmDNnTkIb0A838mxiQ55jbMTiOXZ2dnL06FGWLl3K6tWr+da3vsWWLVtIT0+PwQoH5t133wXgoosuivp7nD9/Pv/3f/9He3t7oCY5Wt3vcfToUc4555y4/x69/PLLefjhh6mrqwuUUxw6dIi2tjYuueSSiPfva3BKMsmf69iQ5xgb8hxjI5bPMSFdLHJycliwYAGbNm2ipaUFn88HQFVVFaWlpQCUlpYGxpL6fD5aW1vJz89PxPKEEMNERUUFhmFwzTXX8Oc//5mKigr+9V//NanlWPv370dRlH7tZJudLAZTh2wYBnv37o1r/bEp3Ijsbdu2Af6+zkIIMdLELSDX19fT0tICgNPpZN26dUybNo0FCxYEagdXrlzJ9ddfD8DixYtZuXIlAC+88AILFy6UAxZCiCBm3e6MGTNYtGgRP/vZz/jTn/7EK6+8krQ17du3j3HjxuFwOKL+milTpmC1WgdVh1xVVUVHR0dCAnJxcTFFRUVBdcjbtm3DYrEEwr4QQowkcQvItbW1LFiwgHPPPZe5c+eyaNEirrvuOu6//34efvhhysvLaWxsZOnSpQAsXbqUxsZGysvLefjhh1m+fHm8liaEGKZ27txJWlpaoM/tvffeS2pqKm+99VbS1rR///5+H5JLSUlh2rRpgwrIZgeLeLZ4MymKwuzZs0N2kKdNm9avHwyEEGK4iFsN8rnnnhv4K7juJk6cyNatW0NeT01N5W9/+1u8liOEGAF27drF2WefjcViAcBmszFz5kw+/PDDpKxH13X279/PZZdd1u+vnTFjBu+///6A752IFm/dzZ49m9dee42uri7S0tLYtm0bV155ZULuLYQQiSaT9IQQw8auXbtC/kp/zpw5fPzxx2ialvD1VFdX09XVNaA2azNmzODYsWOBUrT+2rt3LwUFBSGdfuJl9uzZ6LrOjh07qKuro7a2VuqPhRAjlgRkIcSwUFdXR319fdiA3NHRwf79+xO+JrMzT7RDQrozvw+zTVxvDMPgT3/6U9A0u4qKioTtHgPMmjUL8B/UkwN6QoiRTgKyEGJYMDs+nHvuuUGvz507FyApZRZmKB/oDjL0PXLa4/GwdOlSvvKVr3DjjTeydetWDMOgoqIiIfXHprKyMgoLC/n4448DAXnmzJkJu78QQiSSBGQhxLDQvYNFd1OmTCE9PT0pAXnfvn1kZmYyevTofn/tmDFjyM7O7jUgt7S08JnPfIYnn3yS733ve+Tl5XHVVVfxyiuv0NzcnNAd5O4H9bZt28aECRPIyclJ2P2FECKRJCALIYaFXbt2UVRURGFhYdDrFouFWbNmJW0HeerUqQNqSakoCjNmzIjYC9kcyvHee+/x1FNP8ctf/pK//e1v5OXl8bnPfQ5I3AE90+zZs9mzZw+bN2+W8gohxIgmAVkIMSyEO6BnmjNnDtu2bcPr9SZ0Tfv27RtQ/bFpxowZ7N69O+ygk5UrV1JRUcFrr73GkiVLAP9ApbfeeosxY8YAyQnImqZx/PhxCchCiBFNArIQYsjTNI09e/aE1B+b5s6di8vlCjrEFm8dHR1UVVUNqP7YNGPGDFpbWwNTRLurqKigpKSEhQsXBr0+btw43nvvPf70pz8lfDyteVAP5ICeEGJkk4AshAjL5/Pxhz/8YcBtyGKpsrISl8vV6w4yJPag3ieffAIMrIOFyQz8O3bsCHmvty4VZWVlfOlLXxrwfQdq7Nix5OfnA8FhWQghRhoJyEKIEIZh8P/+3//jG9/4Bk8//XSylxPxgJ5p0qRJZGdn889//jOu6zAMg7q6Ot577z1WrlwJDKyDhSlSQDa7VCS6hKIviqIwZ84cioqKKC4uTvZyhBAibuI2SU8IMXz98pe/5He/+x1A0HjhZNm1axeqqkYMjKqqMnv27LjsILe2trJu3TrWrl3La6+9Rm1tbeC90tJSJk+ePOBrZ2ZmUl5ezvbt24NeP378OJ2dnUMuIAM8+OCD1NfXJ3sZQggRVxKQhRBB/vKXv3DPPfdw66230traOiQC8s6dO5k8eTIOhyPiZ+bOncvDDz+M2+3GbrfH5L4ffvghF198MR6Ph5ycHK666iouuugipkyZwpQpUxg7dmxg7PVAzZw5M9BX2JToMdL9cc455yR7CUIIEXcSkIUQAZs2bWLJkiVccsklPPnkk9x33328/vrrdHV1kZaWlrR17dq1q89DYXPmzMHr9bJz587A8JDBeuutt/B4PLz55pt86lOfwmqN/b8yZ86cyQsvvEBbWxtZWVnA0A7IQghxJpAaZCFEwE9+8hMKCwtZtWoVqampzJ49G13Xwx4iS5Tm5mYOHTrEeeed1+vn4nFQb8eOHZSVlXHFFVfEJRzD6Wl03fshV1RUMGrUqMCBOCGEEIklAVkIAUBTUxNvvfUWX/nKVwLBbPbs2UBy65A3bdqEYRhcdNFFvX5u3Lhx5OfnxzQgb9++Pe7jlM3rd69DHooH9IQQ4kwiAVkIAcCaNWvQNI0bb7wx8FppaSmjRo1KakDeuHEjFouFefPm9fo5RVGYO3duzDpZuFwu9u3b1+fO9WCVlJRQUFAQCMhDtYOFEEKcSSQgCyEAePHFFxkzZkxg1xj8oXPWrFl8/PHHSVvXxo0bOf/880lPT+/zs/PmzWPPnj20tbUN+r4VFRVomhb3gKwoCjNnzgwE5NraWlpbWyUgCyFEEklAFkLQ0dHB66+/zo033oiiKEHvzZ49mz179uB0OhO+Lo/Hw9atW7n44ouj+vz8+fPRdZ2tW7cO+t5m3XW8AzL4yyx2796N1+uVA3pCCDEESEAWQvDqq6/idru54YYbQt6bPXs2mqYFHSJLlG3btuF0OqMOyGYZxqZNmwZ97x07dpCWlsakSZMGfa2+zJw5E7fbzf79+yUgCyHEECABWQjBiy++SGFhIZdccknIe8k8qLdx40aAqANyTk4OZ599dkwC8vbt2zn33HMH3ec4Gt0P6lVUVJCbm8uoUaPifl8hhBDhSUAW4gzndrt55ZVXuP7668OGwTFjxpCfn5+0gDxhwgRKSkqi/pr58+ezadMmdF0f8H0Nw2DHjh0JKa8AmDJlCqmpqWzfvp29e/cyffr0kFIXIYQQiSMBWYgz3Pr162lvbw/qXtGdoijMnj074Qf1DMNg48aNUe8em+bPn09LSwv79+8f8L2PHz9OS0tLwgKy1WplxowZgR1kKa8QQojkkoAsxBnuxRdfJCsri4ULF0b8zOzZs9m9ezculyth6zp06BB1dXX9Dshmv+TBlFkk8oCeaebMmXzwwQc0NDRIQBZCiCSTgCxEEui6zt/+9je6urqSug6Xy8WqVau47rrrsNvtET83e/ZsfD4fu3btStja3n//fYCwddG9Oeuss8jNze1XQH7nnXeoqqoK/LMZkGfMmNGvew/GzJkzA51CJCALIURySUAWIgmeeeYZbr75Zp5++umkruMPf/gDjY2NfP3rX+/1c7NmzQISe1Bv48aN5OTk9DssqqrKhRdeyAcffBDV5/ft28fChQtZsGABra2tgD8gl5eXk5mZ2e91D1T3iX0SkIUQIrkkIAuRYB0dHfzgBz8AYMuWLUlbh9vtZvny5Vx66aVcfvnlvX52/Pjx5ObmJjwgz58/H1Xt/7+m5s+fT0VFBS0tLX1+9kc/+hGpqakcPnyYr371qxiGwfbt2xNaXgH+3WpFUcjMzKS0tDSh9xZCCBFMArIQCfbAAw9QW1vLxIkTYzLQIhyXy8VTTz3F2rVr+eSTT/B6vSGfeeKJJ6iuruanP/1pnx0TzIN6H374YVzW21NTUxMVFRX9rj82mXXIff0AsnXrVv7+979zzz338MADD7Bq1Sr+8z//k4MHDyY8IGdmZlJeXi4dLIQQYgiQgCxEAh07doxf/vKX3HrrrXz1q19l7969MRmL3NMLL7zA1772Na699lqmTJmCw+FgyZIluN1uwL97/Itf/IKLL76418N53V188cXs3LmT5ubmmKxx165dnH322WF3pc364f7WH5suuOACVFXttczCMAzuvfdeCgsL+e53v8tdd93F5z//ef7rv/4LwzASHpABHn/8cX75y18m/L5CCCGCSUAWIoHuvfdeAJYvX84FF1yAYRhxKVuoqKjAarXy7rvv8tRTT/HNb36Tp59+mquvvprW1laefPJJqqqqoto9Ni1cuBBd13n33Xdjssb33nuPiooKrrvuOo4dOxZ4va6ujn//938nOzubuXPnDujamZmZnHPOOb0e1HvjjTd46623+PGPf0xGRgaKovDHP/6Rs846C0hsBwvTlVdeyaWXXprw+wohhAhmTfYChDhTbNq0ieeee44f/ehHjBs3LnAAbOvWrSxYsCCm99q3bx/l5eVceumlXHrppSxZsoT58+fzta99jUsvvZSWlhYuvPBCrrzyyqivOW/ePBwOBxs2bOD6668f9BoPHTpESkoKTqeTa665hvfffx9d11m0aBHHjh3j9ddfJy0tbcDXv+iii/jzn/+Mrushdcy6rvODH/yACRMm8M1vfjPwelZWFq+88gpr165l7NixA763EEKI4U12kIVIAF3XueuuuyguLuaee+4BIC8vj0mTJvHPf/4z5vfbt28fU6dODXrty1/+MmvXruXw4cMcP368X7vHAHa7nUsuuYQNGzbEZI0HDx6kvLycF198kf379/P5z3+eT3/60+zfv5/Vq1cPeid1/vz5tLW1sWfPnpD3Vq9ezbZt2/j5z39OSkpK0Hvl5eXccccdUgcshBBnMAnIQiTAc889x5YtW7jvvvvIyMgIvH7BBRfE/KCe1+ulsrIyJCADLFq0iA8++IDHHnuMT3/60/2+9sKFC9m9ezd1dXWDXuehQ4eYOHEiCxcu5A9/+AMbNmxg+/btvPDCCyxatGjQ1//Upz4FEDbQr1mzhry8PG699dZB30cIIcTIIwFZiDjr6uri3nvvZdasWdx2221B711wwQUcP36c2tramN3v8OHDeL3esAEZ/O3EvvWtbw1oh/SKK64A4O233x7MEjEMg0OHDjFp0iQAlixZwrPPPsurr77KZz/72UFd2zRu3DjKy8t58803Q+69fv16FixYgMViicm9hBBCjCwSkIWIswcffJCqqip+/etfh9TCXnDBBQAxLbPYt28fANOmTYvZNU3nn38+2dnZrF+/flDXaWhooKOjg4kTJwZe+5d/+Zd+1URHY9GiRbz99ttBbe4OHTrEsWPHou7eIYQQ4swjAVmIOKqurub+++/npptuCltTe/7552OxWGJaZrF3714ApkyZErNrmqxWK5/61KcGXYd86NAhgKCAHA9XXnklHR0dQf2QzbVLQBZCCBGJBGQh4ug//uM/8Pl8PPDAA2HfdzgczJgxI6YBed++fRQXF5OdnR2za3a3cOFCDh48yNGjRwd8jUQF5AULFqCqalCZxYYNGyguLo7LDxBCCCFGBgnIQsRJW1sbzzzzDN/61reYMGFCxM9dcMEF/POf/0TX9ZjcN1wHi1gyd17feuutAV/DDMjjx4+PxZIiys3NZc6cOaxbtw7w1x9v2LCBhQsXSpcKIYQQEUlAFiJOtm3bhmEYfXZkuOCCC2hpaaGysnLQ9zQMI+4B+eyzz6awsHBQZRYHDx6kuLh4UH2Oo3XllVeyZcsW2traqKio4OTJk4HDhkIIIUQ4EpCFiJOPP/4YgNmzZ/f6uVge1Dt58iQtLS1xDciqqrJw4UI2bNiAYRgDuobZ4i0RFi1ahKZpvPPOO1J/LIQQIioSkIWIk48++ojS0lKKiop6/dz06dNJT0+PSR2y2cEingEZ/AGzurqaAwcODOjru7d4i7f58+fjcDhYt24d69evZ+LEiYwbNy4h9xZCCDE8SUAWIk4++uijPnePASwWC7NmzWLTpk2DvmeiAvJll10GwMaNG/v9tW63m6qqqoTtINvtdi677DJef/113n77bdk9FkII0ScJyELEQXt7O/v372fWrFlRff7qq6/mn//8J4cPHx7Uffft20d6ejplZWWDuk5fzjrrLHJzc/nggw/6/bVHjx7FMIyEBWTwl1l88skntLa2SkAWQgjRp7gF5OPHj7NgwQKmT5/O2WefzSOPPAJAU1MTixYtYvLkySxatIjm5mbAf7jojjvuoLy8nHPPPTdQvynEcLRjxw4Mw4hqBxngy1/+Moqi8Mwzzwzqvnv37mXKlCkhA0liTVVVLrzwwj53vTVNY/369TQ2NgZeS1SLt+66DyBZsGBBwu4rhBBieIrbf0WtVisPPfQQFRUVbN68mccee4yKigqWL1/OFVdcwYEDB7jiiitYvnw5AK+++ioHDhzgwIEDrFixgttvvz1eSxMi7j766COg7wN6prFjx7JgwQKefvrpAR98g/i3eOvuoosuoqKigpaWlpD3WlpaeOihhygvL+fKK6/knnvuCbyXjIA8Y8YMCgsLmT59OqNHj07YfYUQQgxPcQvIxcXFgb9ezszMZNq0aVRXV7N69WqWLFkCwJIlS1i1ahUAq1ev5rbbbkNRFC688EJaWlqora2N1/KEiKuPPvqI4uJiiouLo/6a2267jYMHDw6obAGgq6uLo0ePJiwgz58/H8MwgqbUAbz77ruUlZXxve99j7Fjx/K5z32O119/nSNHjgD+Fm+pqakJDaqqqvKb3/yGBx98MGH3FEIIMXxZE3GTI0eOsG3bNubNm0ddXV0gNIwePZq6ujrAP5J3zJgxga8pKyujuro6JGCsWLGCFStWAHDixAlqamoS8S0MO/X19cleQkLdf//9gR+2ANLS0vjjH/846G4FA32OW7ZsYfr06f36/XnRRRfhcDj4zW9+0+tgkUh2794NwKhRoxLy52LMmDGoqsobb7zBjBkzAq//8pe/xOFw8OKLL3LOOedQU1PDmjVrWL58OT/5yU+oqKhg7NixCf8BeP78+QDD+t8ZZ9qf63iR5xgb8hxjQ55jbMT6OcY9IHd0dPD5z3+eX//612RlZQW9pyhKv6dZLVu2jGXLlgEwZ84cSkpKYrbWkeZMeTZOp5MnnniCSZMmcd555wHw17/+lZUrV/Lb3/520Nfv73Ps7OyksrKSW2+9td9fe9NNN/Hyyy/z+9//HofD0etnOzo6eOedd2hqagLgww8/BPxBO1G/9jNmzGDXrl2B+zmdTt566y2+8pWvcNVVVwH+53fttdfy/PPP8+CDD1JTU8OUKVPOmN+fsSbPLTbkOcaGPMfYkOcYG7F8jnENyF6vl89//vN86Utf4sYbbwSgqKiI2tpaiouLqa2tZdSoUQCUlpZy/PjxwNdWVVVRWloaz+WJEWLdunV0dnby4IMPBqbWORwOnnrqKf7zP/8z4TWnO3bsQNf1qOuPu1uyZAnPPPMMa9as4eabbw553+128/jjj/PKK6/w7rvv4vV6g97Pyspi8uTJA157f82fP58///nP6LqOqqqBXwvzz7tp6dKlvPzyyzz99NMcOnRIDsoJIYQY0uJWg2wYBkuXLmXatGl897vfDby+ePFiVq5cCcDKlSu5/vrrA6+bB5Q2b95MdnZ2v+o3xZnrxRdfJCcnh8svvzzw2ve+9z08Hg+PPvpozO+3Y8cOrr322oh/Vd/fA3rdXX755ZSVlQX+jPS0fPlyvvvd73LixAm+853vsGHDBiorKwP/O3LkCKmpqf2+70DNnz8/MMIZwv9agP9ZzJ07l/vuu4+Ojo6EHtATQggh+ituAXnjxo0888wzbNiwgZkzZzJz5kzWrl3Lvffey7p165g8eTJvvvkm9957LwDXXHMNEydOpLy8nG984xs8/vjj8VqaGEG8Xi8vv/wyixcvxmazBV6fPHkyN954I48//jjt7e0xvef3v//9wO/lcD766COKiooG9Fc9FouFL3/5y7z++uucOHEi5P2XXnqJSy65hN27d/PAAw+wYMECJk2aFPhfbm5uv+85GBdddBEAH3zwAV6vlzVr1vDZz3426NcC/OVUd955J9XV1UBiO1gIIYQQ/RW3gHzJJZdgGAY7d+5k+/btbN++nWuuuYb8/HzWr1/PgQMHePPNN8nLywP8/wF97LHHOHjwILt27WLOnDnxWpoYQd59912am5u54YYbQt67++67aWlp4fe//33M7rdx40bWrVtHeXk5zzzzDJs3bw75zEcffcSsWbP6XV9vuu2229A0jT//+c9Brx89epQdO3YE/tZlKJg0aRIFBQVs2rSJd999l6amppDyCtMXvvCFQLmLBGQhhBBDmUzSE8Paiy++SFpaWuBAWHcXXHABl19+OQ8//DAejycm9/vZz35GYWEh77//PsXFxdx5553ouh54v6uri4qKigGVV5imTZvG3Llzefrpp4NeX7NmDeAvRxoqFEVh/vz5bNq0qddfC4CUlBTuuususrOzB9SlQwghhEgUCchi2NJ1nZdeeonPfOYzpKWlhf3M3XffTXV1Nc8999yg72fuHt99990UFRWxfPlytm7dyp/+9KfAZ3bu3DngA3rdLVmyhB07drBjx47Aay+//DJTpkzhrLPOGtS1Y23+/Pns37+fv/zlL73+WoC/POXo0aN9dugQQgghkkkCshi2tmzZQm1tbcS/0ge4+uqrOeecc2LS7s3cPTanPH75y1/mggsu4N577+X48eM8//zz/OhHPwIGdkCvu1tvvRWbzRbYRW5tbeXtt98eUrvHJrMOubGxsddfC/DvOGdnZydiWUIIIcSASUAWw9aLL76IzWbj2muvjfgZRVG45ZZb2LJlS9hDb9Hqvnucnp4O+KezPfLII9TW1jJ27Fi++MUvsnPnTu68807KysoGfC+A/Px8rrvuOp599ll8Ph+vvfYaXq93SAbkOXPmYLFY+vy1EEIIIYYLCchiSOnq6mLbtm19fs4wDF588UWuvPLKPnckFy9ejGEYvPLKKwNe0+23386oUaMCu8emCy+8kEceeYQf/vCHbNq0iRMnTvDrX/96wAf0urvtttuoq6vjjTfe4OWXX6agoCAwDW4oSU9P55JLLuGzn/2s7A4LIYQYERIyalqIaP3617/mRz/6Ebt372b69OlhP+Pz+fj2t7/NoUOH+MlPftLnNWfMmMG4ceN4+eWXWbp0ab/WYxgGt99+O7t372bt2rWB3ePu7rjjjn5dM1pm15cnnniCDRs28LnPfQ6LxRKXew3W2rVrUVX5eVsIIcTIIP9FE0PKu+++i2EY/PKXvwz7fltbG9dddx0rVqzgBz/4AV/5ylf6vKaiKCxevJh169bR1dXVr/U8++yzPP300/zkJz/h6quv7tfXDlZKSgpf/OIXefHFF2lpaRmS5RWmtLS0hA4oEUIIIeJJArIYMnRdZ/PmzVitVp599lmqqqqC3q+qquKSSy7hzTff5Pe//z333Xdf1LuWixcvxul08uabb0a9no8++ogf//jHfPrTn+bHP/5xv76XWFmyZAkAdrs9MEZbCCGEEPElAVn06ejRo7z00kuB/7333nsYhhHz++zdu5fW1lZ+9KMfoes6jzzySOA9l8vFddddx5EjR1i7di1f//rX+3Xtyy67jKysLF5++eWoPr9z505uvPFGCgoK+NOf/pS00obZs2dz/vnnc+2115KRkZGUNQghhBBnGqlBFn363Oc+x/bt24Ne++Mf/8jXvva1mN5n06ZNAHzxi1/kk08+4Xe/+x0//OEPycnJ4bvf/S47duzglVdeiTiIojcpKSlcc801rFmzBl3XI+48G4bB//3f//H973+f3NxcnnzySQoKCgb1fQ2Goii88847WK3yR1UIIYRIFNlBFr2qrKxk+/bt/Md//EdgZPiFF17ID37wA9ra2mJ6r02bNpGfn8/kyZP5/ve/T3t7O7/97W/561//ym9+8xu+//3vc8011wz4+osXL+bkyZNs3bo17PuNjY189rOf5Y477uDKK69k586dnHvuuQO+X6xkZmbKYA0hhBAigSQgi1699NJLACxbtozzzjuP8847j//93/+lrq6O//mf/4npvTZt2sT8+fNRFIWZM2fy6U9/mocffpivf/3rzJ8/f9D3u/rqq7FarRHLLO6++27WrVvH//7v/7JmzRoKCwsHdT8hhBBCDE8SkEWvXnzxRWbPns24ceMCr82dO5evfvWr/OpXv+LAgQMxuU9TUxN79+4N6vN79913U19fj9Vq5bnnnsNmsw3qHrm5uVx22WWsXr065D2Px8Pf//53br31Vv7t3/4tJn2MhRBCCDE8SUAWEVVXV7N58+aw44Pvu+8+7HY73/ve92Jyry1btgAEBeQFCxbwn//5n6xatSoooA/G5z73OSoqKti5c2fQ6+vWraO1tZWbb745JvcRQgghxPAlAVlEtGrVKoCwAbm4uJgf/ehHvPzyy7zxxhuDvtemTZuwWCzMnTs38JqiKPz0pz/lsssuG/T1TV/60pdwOBz87//+b9Drf/3rX8nOzpZWakIIIYSQgCwie/HFF5k6dSpTp04N+/53vvMdxo8fz/Llywd9rw8++IBzzz037q3M8vLyuO2223j22WdpaGgAwO12s2rVKm644QZSUlLien8hhBBCDH0SkEVYDQ0NvPPOO2F3j012u52bbrqJ999/n87OzgHfS9M0tmzZElReEU933HEHLpeLFStWAPDGG2/Q1tYm5RVCCCGEACQgiwjWrFmDpmm9BmSARYsW4fV6effddwd8rz179tDR0cFFF1004Gv0x/Tp01m0aBGPPfYYXq+Xv/71r+Tm5nLFFVck5P5CCCGEGNokIIuwXnzxRcaOHcusWbN6/dyll16K3W7v1wjnnswBIYnaQQa48847qamp4U9/+hOrV6/mxhtvlPIKIYQQQgAySS/pqqur2bNnT+CfCwoK+gyl8dbe3s4bb7zBt771rT7bnTkcDi6++GLWrVs3oHu98cYb/Od//idjx45lwoQJA7rGQHzmM59h8uTJ3HnnnbS3t0t5hRBCCCECZAc5ibxeL5/61Kf49Kc/Hfjf7Nmz+ctf/pLUdb355pt4PB4WL14c1ecXLVrErl27OHHiRNT38Hg8fO973+PTn/40+fn5vPLKKwntPayqKv/2b/9Ge3s7+fn5LFiwIGH3FkIIIcTQJgE5iZ566ikOHjzIY489xsaNG9m4cSOzZs3ie9/7Hl1dXUlb19q1a8nMzOSSSy6J6vNma7Royyz279/P/Pnzeeihh/j2t7/NP//5T84555wBr3egvvrVr5KXl8ctt9wy6CEkQgghhBg5JCAnicvl4r/+67+YN28et99+OxdddBEXXXQRjzzyCFVVVTzwwANJWZdhGKxdu5arrroq6tB4/vnnk5+f32dANgyDP/7xj8yaNYujR4+yevVq/u///g+HwxGLpfdbZmYme/bs4cEHH0zK/YUQQggxNElATpIVK1ZQVVXFf//3fweVFlxyySXceuut3H///Rw7diym99yyZQtf/epXqauri/iZnTt3UlNTwzXXXBP1dVVV5YorrmDdunUYhhH2M62trdxyyy0sXbqUCy+8kB07dkRdwhFPo0ePTlpAF0IIIcTQJAE5CTo7O7nvvvu4/PLLw7YWu//++1EUhbvvvjum97333ntZuXIlF154IXv37g37mbVr1wJw9dVX9+vaV155JTU1NRGv+8Mf/pAXX3yR5cuXs27dOkpLS/u3eCGEEEKIBJGAnASPPfYYdXV1IbvHprFjx3LPPffwl7/8ZVD9hbvbs2cPb7/9NrfddhtOp5OLLrqIt99+O+Rza9eu5fzzz6ekpKRf1++rDvn111/nmmuu4Z577kFV5bedEEIIIYYuSSoJ1tbWxv33389nPvMZLr744oif+/73v8+YMWP48Y9/HJP7Pv7449jtdh566CE2b95McXExV111FWvWrAl8prm5mQ8++IBrr72239cfP3485eXlYdu9HTt2jMrKShYuXDio70EIIYQQIhEkIMeIx+Phk08+6fNzjz76KE1NTfzXf/1Xr59LS0vj29/+Nu+++y779+8f1Nra2tp4+umnueWWWygoKGD8+PF88MEHzJgxg6997WuBmuQ33ngDXdf7VX/c3aJFi3j77bfxeDxBr7/11lsAMqlOCCGEEMOCBOQY+fGPf8yUKVOYN28ezz77bEhIBP8Ajocffphrr72WOXPm9HnNr371q1itVv7whz8Mam3PPPMMHR0dfPvb3w68lpOTE3j99ttvD3SvyMvL44ILLhjQfa677jo6Ojp47bXXgl7fsGEDhYWFnH322YP6PoQQQgghEkECcgy0tbXx29/+lrlz59LS0sKXv/xlxo0bxyuvvBL0uccee4ympiZ++tOfRnXdoqIiFi9ezFNPPYXb7R7Q2gzD4LHHHmPOnDkhwXf69On8/Oc/56WXXuLPf/4zr776KldffTUWi2VA97rqqqsYNWoUTz/9dND9169fz4IFC6T2WAghhBDDgiSWGPjDH/5AW1sbjz/+OHv37mXt2rWMGjWKm2++mW3btgHQ0dHBgw8+yGc+8xnmzp0b9bW/8Y1v0NDQwOrVqwe0trfffpu9e/cG7R53993vfpcLL7yQpUuXUl9fP+DyCgCr1cq//Mu/sGbNGpqbmwE4cOAA1dXVUn8shBBCiGFDAvIg+Xw+HnnkES677DLmzJmDqqp85jOf4fXXXyc/P5/Pfvaz1NTU8Pjjj9PY2Bj17rFp0aJFjBs3jt///vf9XpthGPz85z8PTIsLx2L5/9u796Coyv8P4O/1RjKCjiKKrJMiK8iyIEJTqFnJOFwSmZwglUjD2iInplSSUbOaKUWzhpkmmUBMYNDCvJCJjUlgoTCDXDSyGfGCcvGy5FCoKaw83z/4ubOo+PMsZ/fs4vv1lz57Lp/znh2fj2eePWcgtm/fDpVKBZVKhfDwcMnnMZeQkICOjg4UFBQA6F5eAYANMhERETkMNsh9tHv3bly8eBErVqzoMT527Fjs378fbW1tiImJwebNmxEeHo6nn35a0vEHDhyIpUuX4vDhwzh37pykfXNzc1FSUoL169c/9GUYPj4+2Lp1K1atWgU3NzdJ57hXUFAQtFot8vLyAHQ3yGq1Gt7e3n06LhEREZGtsEHuAyEEvvjiC2g0GsydO/e+zwMDA7Fjxw5UVVXBYDBg3bp1Fp3n9ddfx4ABA5Cdnf3I+/z9999Yvnw5ZsyYgTfffPP/3T4+Ph4bNmywqD5zKpUKCQkJOHr0KOrr61FSUoLZs2c/8HnPRERERPaIDXIfHD16FJWVlXj//fd7/QHavHnzsG3bNqxevRrTp0+36DxqtRpRUVHYtm0bOjs77/v8v//+Q1NTU4+xjz/+GO3t7cjMzLT5j+Pi4+OhUqmwatUqtLa2cnkFERERORQ2yBY6c+YMEhMTMWrUKCxevPih2y5ZsgSfffZZn86XlJSEy5cv47vvvrvvM71ej/HjxyMiIgIHDx7EoUOHsGfPHqSmpsLPz69P57WEWq1GWFgY9u7dC4Drj4mIiMixsEG2wLFjxxAaGopr166hsLAQzs7OVj9nZGQk/P39sWnTJgghTONnzpzBjh07MHPmTJw4cQJRUVGIjIyEl5cXVq9ebfW6epOQkAAA0Gg0GD9+vGJ1EBEREUnFBlmiXbt2Yfbs2RgxYgTKy8sf+rpoOalUKqSkpKCurg4HDx40jW/cuBGDBw9GQUEBLly4gPz8fERGRiI9PR1PPPGETWp7kPnz58PV1bXPT8UgIiIisjU2yBLU1dXhlVdeQUhICMrLy6HRaGx6/gULFkCtVmPjxo0AgMbGRuTk5GDp0qXw8PDAkCFDsGjRIvz0008IDg62aW33GjZsGGpra7F+/XpF6yAiIiKSig2yBP7+/vj+++9x+PDhPj8OzRJDhgzB8uXL8dtvv6GiogKbN2+GEAIffPCBzWt5FBMnToSLi4vSZRARERFJwgZZotjYWEWXLrzxxhsYMWIEVq9ejaysLNNrrYmIiIhIHlZrkBMTE+Hu7g5/f3/T2LVr1zBnzhxoNBrMmTPH9DpiIQSSk5Ph7e2NgIAAVFdXW6ssh+fi4oJly5ahpKQEt27dQmpqqtIlEREREfUrVmuQlyxZgp9//rnHWFpaGsLCwlBfX4+wsDCkpaUBAA4ePIj6+nrU19cjMzMTSUlJ1iqrX3j33XcxdOhQxMXFwcfHR+lyiIiIiPoVqzXIs2bNwsiRI3uMFRYWmp4ZvHjxYuzbt880/tprr0GlUuGZZ55BW1sbLl26ZK3SHN6YMWNQU1ODzMxMpUshIiIi6ndsugb5ypUr8PDwAACMHTsWV65cAQA0Nzf3eFauWq1Gc3OzLUtzOD4+PnB1dVW6DCIiIqJ+Z5BSJ1apVFCpVJL3y8zMNN05vXz5MlpaWuQurV8wGAxKl9AvMEd5MEd5MEd5MEd5MEd5MEd5yJ2jTRvkMWPG4NKlS/Dw8MClS5fg7u4OAPD09ERjY6Npu6amJnh6ej7wGHq9Hnq9HgAQEhKCcePGWb9wB8Vs5MEc5cEc5cEc5cEc5cEc5cEc5SFnjjZdYjFv3jzk5OQAAHJychATE2Maz83NhRACFRUVGD58uGkpBhERERGRLVntDvLChQtRWlqK1tZWqNVqfPLJJ0hNTUVcXByys7Px5JNPoqCgAAAQFRWFoqIieHt7w9nZGd9++621yiIiIiIieiirNcg7d+584HhxcfF9YyqVCl9//bW1SiEiIiIiemR8kx4RERERkRk2yEREREREZtggExERERGZYYNMRERERGSGDTIRERERkRk2yEREREREZtggExERERGZYYNMRERERGSGDTIRERERkRk2yEREREREZlRCCKF0EZZyc3PDhAkTlC7DLhkMBowePVrpMhwec5QHc5QHc5QHc5QHc5QHc5RHbzk2NDSgtbVV8vEcukGm3oWEhOD48eNKl+HwmKM8mKM8mKM8mKM8mKM8mKM85M6RSyyIiIiIiMywQSYiIiIiMsMGuZ/S6/VKl9AvMEd5MEd5MEd5MEd5MEd5MEd5yJ0j1yATEREREZnhHWQiIiIiIjNskImIiIiIzLBBdhCJiYlwd3eHv7+/aezEiRMIDQ2FTqdDdHQ0/v33XwDdz/wbOnQopk6diqlTp+Ltt9827VNVVQWdTgdvb28kJyfjcVthI0eON2/exIsvvghfX19otVqkpqYqci1Kkuv7eNe8efN6HOtxIVeOHR0d0Ov1mDx5Mnx9fbF7926bX4uS5Mpx586d0Ol0CAgIQEREhEXPTnVkUnIEgJMnTyI0NBRarRY6nQ63bt0CwHlGjhw5z3ST6zt5l6S5RpBDOHLkiKiqqhJardY0FhISIkpLS4UQQmRnZ4u1a9cKIYQ4f/58j+3MPfXUU6K8vFx0dXWJiIgIUVRUZP3i7YgcOd64cUP8+uuvQgghbt++LWbOnMkchWXfRyGE2L17t1i4cOFDt+mv5Mpx3bp1Ys2aNUIIIe7cuSMMBoOVK7cvcuTY2dkpRo8ebcouJSVFfPTRR9Yv3o5IybGzs1PodDpRW1srhBCitbVVGI1GIQTnGTly5DzTTa7vpBDS5xo2yA7k3n/YXV1dRVdXlxBCiIsXL4opU6Y8cLu7WlpahI+Pj+nvO3bsEHq93spV25++5niv5ORkkZmZaZ1i7ZgcOba3t4sZM2aIP//887FskIWQJ0e1Wi2uX79u/WLtWF9z7OjoEG5ubqKhoUF0dXWJt956S3zzzTe2Kd6OPGqOBw4cEPHx8fftz3mmW19zvNfjOs8IIU+Wlsw1XGLhwLRaLQoLCwEAu3btQmNjo+mz8+fPIygoCM899xx+//13AEBzczPUarVpG7VajebmZtsWbYek5miura0N+/fvR1hYmM3qtVeW5Pjhhx9ixYoVcHZ2tnm99kpqjm1tbQC6s5w2bRpiY2Nx5coVm9dtb6TmOHjwYGRkZECn02HcuHE4deoUli5dqkjt9qS3HE+fPg2VSoXw8HBMmzYNmzZtAsB5pjdSczTHeaYnS7K0ZK5hg+zAtm3bhi1btiA4OBjt7e0YMmQIAMDDwwMXL15ETU0NvvzySyxatKjHGh3qydIcjUYjFi5ciOTkZHh5eSlVvt2QmmNtbS3Onj2Ll156SeHK7YvUHI1GI5qamjB9+nRUV1cjNDQUK1euVPgqlCc1x87OTmRkZKCmpgYtLS0ICAjAhg0bFL4K5fWWo9FoRFlZGfLz81FWVoa9e/eiuLhY4Wrtl6U5cp65n9QsLZ1rBlmjeLINX19fHDp0CED3/5wOHDgAAHBycoKTkxMAIDg4GJMmTcLp06fh6emJpqYm0/5NTU3w9PS0feF2RmqOISEhALofSq7RaPDee+8pUre9kZpjZWUljh8/jgkTJsBoNOLq1at4/vnnUVpaqtQl2AWpOQYHB8PZ2Rnz588HAMTGxiI7O1uZ4u2I1BzF//2QbNKkSQCAuLg4pKWlKVC5fektR7VajVmzZsHNzQ0AEBUVherqarz66qucZx5Aao537xZznrmf1CyHDRtm0VzDO8gO7OrVqwCArq4ufPrpp6ZfYxsMBty5cwcAcO7cOdTX18PLywseHh5wdXVFRUUFhBDIzc1FTEyMYvXbC6k5AsDatWvxzz//ID09XZGa7ZHUHJOSktDS0oKGhgaUlZVh8uTJj31zDEjPUaVSITo62pRdcXEx/Pz8FKndnkjN0dPTE6dOnYLBYAAA/PLLL5gyZYoyxduR3nIMDw/HH3/8gZs3b8JoNOLIkSPw8/PjPNMLqTkCnGd6IzVLi+eaviycJttZsGCBGDt2rBg0aJDw9PQUW7duFenp6UKj0QiNRiNWrVplWrT+ww8/CD8/PxEYGCiCgoLEjz/+aDpOZWWl0Gq1wsvLSyxbtsy0z+NCjhwbGxsFAOHr6ysCAwNFYGCgyMrKUvKybE6u7+Ndj/qDyP5GrhwbGhrEs88+K3Q6nZg9e7a4cOGCUpekCLlyzMjIEL6+vkKn04m5c+eK1tZWpS5JEVJyFEKIvLw84efnJ7RarUhJSTGNc57pe46cZ7rJ9Z28S8pcw1dNExERERGZ4RILIiIiIiIzbJCJiIiIiMywQSYiIiIiMsMGmYiIiIjIDBtkIiIiIiIzbJCJiOxYW1sbtmzZAgBoaWnByy+/rHBFRET9Hx/zRkRkxxoaGjB37lzU1dUpXQoR0WODr5omIrJjqampOHv2LKZOnQqNRoO//voLdXV12L59O/bt24cbN26gvr4eK1euREdHB/Ly8uDk5ISioiKMHDkSZ8+exbJly2AwGODs7IysrCz4+voqfVlERHaNSyyIiOxYWloaJk2ahNraWnz++ec9Pqurq8OePXtQWVmJNWvWwNnZGTU1NQgNDUVubi4AQK/X46uvvkJVVRU2b96Md955R4nLICJyKLyDTETkoF544QW4uLjAxcUFw4cPR3R0NABAp9Ph5MmTuH79Oo4dO4bY2FjTPrdv31aqXCIih8EGmYjIQTk5OZn+PGDAANPfBwwYAKPRiK6uLowYMQK1tbUKVUhE5Ji4xIKIyI65uLigvb3don1dXV0xceJE7Nq1CwAghMCJEyfkLI+IqF9ig0xEZMdGjRqFGTNmwN/fHykpKZL3z8/PR3Z2NgIDA6HValFYWGiFKomI+hc+5o2IiIiIyAzvIBMRERERmWGDTERERERkhg0yEREREZEZNshERERERGbYIBMRERERmWGDTERERERkhg0yEREREZGZ/wEYiTzUuN9IRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from kats.models.sarima import SARIMAModel, SARIMAParams\n",
    "warnings.simplefilter(action='ignore')\n",
    "\n",
    "# create SARIMA param class\n",
    "params = SARIMAParams(\n",
    "    p = 2, \n",
    "    d=1, \n",
    "    q=1, \n",
    "    trend = 'ct', \n",
    "    seasonal_order=(1,0,1,12)\n",
    "    )\n",
    "\n",
    "# initiate SARIMA model\n",
    "m = SARIMAModel(data=air_passengers_ts, params=params)\n",
    "\n",
    "# fit SARIMA model\n",
    "m.fit()\n",
    "\n",
    "# generate forecast values\n",
    "fcst = m.predict(\n",
    "    steps=30, \n",
    "    freq=\"MS\"\n",
    "    )\n",
    "\n",
    "# make plot to visualize\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "image/png": 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Norm9I4Tq1N22wSUZfTe/mSzOqJ8L6LQCW6eGopc10xJjR7U7WhM3/NVtQ/fWlIGAAZmIiIhiqqWlBWlpaRAEAXfffTf27t2L/fv3x/pYAICWjoBs0Ct77EEGfJMkYqHVZAfQcbZ+JkUYdSo4XB44XZ5oHM1f3TZoVf2eLR4NvBMTERHRoCJVkAHg1ltvhVarxerVq2N8Kh8pICcZNd1uhEtO0ADw3YwWCy0mqT9a02+V1qhXw+n0wOmKzg2FUnXbYFBDMQDTZkSP3NraikWLFmHs2LEYN24cduzYgebmZixYsACjRo3CggUL0NLSAgAQRRH33XcfioqKMHnyZOzbty+SRyMiIqI44PV60dLSgtTUVABAWloaxo8fj7KyshifzKfN7IRCAJI6wnBniR0V5FgFZOlsiR2tHn0x6lWwOz1wRSkgt5k7Ksg6ZbfWlIEgoif+6U9/iksvvRRHjx7F119/jXHjxmH58uWYP38+SktLMX/+fCxfvhwAsGHDBpSWlqK0tBQrV67Ej370o0gejYiIiOJAW1sbRFH0V5ABIDU1Fa2trbE7VCetJif0OhW0mu6TGBKNvtBssUd3Q52kTTqbWtXvaxMMarg9YtRuKJSmfxi0vEmvi7a2NmzevBl33XUXAECj0SAlJQVr167FkiVLAABLlizBu+++CwBYu3YtbrvtNgiCgPPOOw+tra2ora2N1PGIiIgoDkh/k9w5IKekpMRNQG4zO3rdBif1INvssVk3La1yDmQRh7Ruut0q3w2Foij2uiSlzeyESilAr1P1O6M5HvX/I0eIKioqMHToUNxxxx34+uuvMX36dDz55JOor69HdnY2ACArKwv19fUAgOrqagwbNsz/9Xl5eaiurva/VrJy5UqsXLkSAFBXV4eamppIfQsDWkNDQ6yPMCjwOsqD11EevI7y4HWUh1zX8dixYwB8YUv6M12j0aCxsTEu/oxvbLFAqxJham1AjdLS5Tlnx4KQltY21FTXhLQQI5zr2NRihkYloq21ATUqS5+vdTt8z9fU1qNmqDxh3u70oLHVhryMhG7P1dS3QqdRwGJqQk1N5KvWcv97HbGA7Ha7sW/fPvzjH//AzJkz8dOf/tTfTiERBAFCP2NJzrR06VIsXboUAFBcXIycnBzZzjzY8NrIg9dRHryO8uB1lAevozzkuI6HDh0CABQVFfnfLycnByaTKS5+nWzOb5CaqEdOTg4y0w3dnlcp98Mr6JCRmQW9LrRYFer3aXN9g+QEPXKys5E91Njna/NPCQBOQKFOQmZWtixtD+0WJ5qtbRiakQ71GVVsi/NbJBq1yMjMQk5OatifFQg5f79ErMUiLy8PeXl5mDlzJgBg0aJF2LdvHzIzM/2tE7W1tcjIyAAA5Obm4uTJk/6vr6qqQm5ubqSOR0RERHGgubkZALr1IFssFrhcsbn5rTOTxQWDXt0tAEqMejVsjti0WJisLhh0KqgCaLFITdICACw2Fzy9tEUEy+sVYbW7YLV3/XVyujwwW10w6ntuTRkIInbqrKwsDBs2zP9XJ5s2bcL48eNx1VVX+Ue3rF69GldffTUA4KqrrsJLL70EURSxc+dOJCcnd2uvICIiosGltx5kADHvQ/Z4RVhsLt8q51421Rl0qpgEZK9XhMXqglGvhiqAEJqS2Dkgy3NWj1eExyui1dS1r9lqd8NicwfcHx2PItZiAQD/+Mc/cOutt8LpdGLkyJF44YUX4PV6ccMNN2DVqlXIz8/Hf//7XwDA5ZdfjvXr16OoqAgGgwEvvPBCJI9GREREcUCqIEtj3oCuAXno0KGxOBYA34Y8Eb4qcW+jyhIMatgc0VvhLDHbXPCK6DO8d9Z5ZrNcYd7t9sKgVaGpzY7hWQn+ttk2sxNWuy8gq9Xdp38MBBENyFOnTsWePXu6Pb5p06ZujwmCgKeeeiqSxyEiIqI409zcDIPBAK1W638sXirI0qiyxD7WJSfoVahtssErRjcgS2dL0Ae2ytmg8wVpk80Nj1eeFgun2wu1SgG3R4Td4fH3YDe12mFzuKHXqaAJ4cbFeDAwT01ERESDQuctehKpmhzrgNzc5lvlbDSoe63SJhjUcDjcUdtQJ5E2/CX0cbbOBEFAokEtb4uF2+sf4WbqWJZid7rRZnZAFH3rrQfiDGSAAZmIiIhiqKWlpVtAlirIUn9yrLSYfCHUqFND2Uufr1Gvht3p6XUecKS0SmfTqwLeVJdoUMNqc8MhU5gvPdkGl8sDnVqB5nbfDxNWu8cflgOtbscjBmQiIiKKmZ4qyPHSYtHS3tHGYOi9EppoVHescI7OhjqJFN4NQVRpE40aWO1uuFyesD/f7fHi/17+Gh/vrIJWo4TJ4puO0Wqy40SdGQAwLDOh1x8s4t3APDURERENCs3NzV1u0APiJyBLfb4piZpeX5NoUEMUAbM1ugG5rWNyRKJRE/CmuiSjBha727/gJBwmiwtuj4jD37ZAEATfNbC50WJy4ttqE9KStUhL1rKCTERERBSsnlosDAYD1Gp1zANyi8kBnUYJvVbd62uSjL7w3G6N7szmVpMTapUCRn3vZztTcoIGVpsLLhnaQUwdK6vrm21obLVDqQROtdjgcnlReqINY/NTIAgIqD86HjEgExERUcz01GIhCAJSUlJi3oPcZnb2O8s3weALqOYoB2TpbMGst05N0sDh8sJmC/+sbZ1mHx8ub4Zeq0K72YnaBiusdjfGFKRAFAFFkBuT4wUDMhEREcWEzWaD3W7v1mIB+NosYl1BbjP1H0L9FWRLjAJyED2+0rKQ5o4JGOFo76ggKwTgUHkLVEoFrHY3jte2AwBGDUuGUqkIuP0j3jAgExERUUz0tGZakpqaGvuAbHbAqO+7gpxo9FWQrTYXxCjOQm639F/dPlNKgi8gm23hLwtpM/l+IBg3IhXHKlvhcnsxNEWH8qp2ZA8xIMHQ+3rugWDgnpyIiIgGtJ7WTEvioYJssrj63QaX0NEDbLW7o7pNTwrIwWyqS02Stum54QmzD7nd4qsgnzshAy63r+/YK4ooO9mOMfkpEEUxqPaPeDNwT05EREQDWl8V5Fj3IIuiCJPVBaNBDVUfN5olGk4HZLlWOAfCZPWF92A21aUl+SrIFhkqyFJAnjw6HWqVAofKm1FRbYLL7cWYgmR4vGJQ7R/xJqKrpomIiIh6IwXkeOxBtth8gTexn2UXBqmC7HBHbd20y+2F3eGBMchFHKlJOgCARYYwb7K6oNMqodMoMXp4Mg6Xt0CvVUEQgNHDU+D1ikG1f8SbgXtyIiIiGtDiuQdZ2lTnW+Xce1xSKgTotUrYHR7ZVjj3R5rP3NcCk54kJ2igEACLNfx10yaLyze5wuLExMI0nGqxYeehegzPSoBBp4LLI0KvG7h1WAZkIiIiion+epAdDgdsNlu0jwXg9KQH3yrnvkOoQaeKag+yPyDr+w7vZ1IoBBj0apht4fcgmyxO6DRK2J0ejC/0/Q1Ac5sDYwtS/K9JCGJGc7xhQCYiIqKYaG5uhlKpRGJiYrfnYr1Nr6W9cwW574CcYPCtm45WD7K0ZjqQ8H6mRIMaFqsLrjC36ZmsLui1Shh0KiQa1MhI0wMAxuSnwOPxQqUUoNMGfgNhvGFAJiIiopiQ1kwLPSyTiJeAHEifr1Gvhs0RvZv0WtrsHZ+rCnpTXaJRLcu6aYvN12Jh1KvhcHkwZVQ6tBolCvOSYHd6kJak7fHXdaAYuM0hRERENKD1tEVPIt24F6uALPUgpyT0H/QSDGo0tNjgdHqicTS0drRYGPWaoCvIyUYNmttMcLrCO6vZ5kZepgpJRg3aTE5cef5wzD0nGxq1ElaHx7+UZKBiBZmIiOgsIYoinnzySRw4cCDWRwHg60HuLSBLFeRYjXprMTmgUStg1PdfS0zQq2F3eOCIUkCW1jwnGtVBV2mTEzWw2FxhVZC9XhEWm2/MnF6rhF7na6UYkuKbkiFAhGEA36AHMCATERGdNTZs2ID7778fzz//fKyPAqDvCnKsWyz8q5wDGFWW2NGD7HBHr4Ks7xixFqzURC1sDg/sjtDPanW4IYqAQauCQiFgSIoeto4fDpwuD/RaFTRBLDCJRwzIREREZwGXy4X/9//+H4DYhc4zST3IPYl1QG41OWEMcJVzgkENl9sLm90dhZMBzW32oNdMS1ISfdv02jpaSEJhtvjWTBv0SigVApKMav8ED7vTg/RkXcjvHS8Gdv2biIiIAvLss8/i6NGj0Gg0Md1Q11ncV5D1amgDqNImGdUdX+OK9LHg8YpoarMjwaAOaZWz1BvcbnHC6xWhCLKHGQDarR090DrfhA+tWgmVSgGPxwuvV0SiURP0e8YbBmQiIqJBrqWlBY888gjmzZsHt9sdFwHZ4/Ggra2t14Cs1Wqh1+tjdlaTxYm0pESoAmmx6AiEZpsTHq8Y9I1zwWg3O2GxuZGapIVaFVqLBXB6U2AoAVmaw6zXqaAQBAiCgCHJOjS22gABA3pBiIQtFkRERIPc73//e7S0tGDFihUx31Ankc7QW0AGYrduWhRFmKwuGPUqqAJYxJGW5AudZmv4CzgkTpcH9U3Wbo/XNlpg7bhBLpQWi9SOs/rWTYd2VukmQb1W5V9UkpKohdXuQUqCNqI/IEQLAzIREdEgVlpain/+85+46667MGXKFKSmpsZFBVlaM91bD7L0XCwCss3hgdsjIjGAJSEAkN4xvcEkwwpnicPpwbdV7WjumHkMAFa7G+0WJyx2N4wB3kB4prSO/mCLzQV3iGc1SS0WnRaVJOhV0GmUSEse2OPdJAzIREREg9jbb78Nl8uFRx99FEDsQueZpIAcjxVkaQZygkEdUAU5vSMUmsxOuGWqIHu8IiAAZVVt/pv/Glps8HhFuD0iDPrAwvuZpJv0LGGEeanFItFwepW0UqlAzlAjEg0Dv/8YYEAmIiIa1E6dOgWDwYCcnBwAvtBpNpvhckX+hrK+SFXs/gJyLKrdzf4104Et4kg0aKBQCGi3hl6VPZPHK/pugFMpUVbVBofTg1PNNn+oNeqVUCqCj3EqpQJ6rRJmW+ib/0wWF9QqRbde49wMY0A3NQ4EDMhERESDWGNjI4YOHer/51hvqJPEcwVZamsw6JUBVWkVHaPOTBYX3B55ZiE7XR4oBAF6nQo2hwflVe0QIeJwue8HhtyhxpB7fRONGpitLnhCPGu7xQWdRhlSD/RAMXi/MyIiIkJjYyOGDBni/+dYj0+TxHMPcovUYhHEKuckowZmqxN2p1w36Xn9n51sVMNkdcKgVWHb13UoyElE1hBjSC0WgK81wmJ3w+EK7axmqwt6rTKgCR8D1eD9zoiIiAgNDQ1dArIUSGN9o14gAVmqIIuiPG0LgWrtmNKQkqgJeJVzSqIGZpsbTpdMFWSnxz+CTRAEpCZqUXXKgtpGK2ZPyQJEhFxBTk7QwGpzBXzWpjZ7l3YMk9UJnVYFdQD92QPV4P3OiIiIKG5bLFpaWpCYmAi1Wt3ra1JSUuDxeGA2m6N4Mt9NemqVAgn63s92ptQkLcxWF5zu8CvIHq+IXz29C5t2VXd5fNvXddCqFZg+Nh2CAv4Ra8FKTtDAYnfDFUAF2eH04NtqE6y20z3rpo4KciiLSgaKwfudERERUa8tFvFQQe6r/xiIXTtIm8kJQ5Bj1NKTdbDYXLDLsG7aZHGiuc2B9dtOoPRkGwDA7nBj75EGTB83FBq1MqzqbUqiFja7GzZn/xXkNrMDZqsTbRan/zGL1QW9VhXSopKBggGZiIhokHI4HDCZTHHZYtHU1NRnewUQu2p3a0dADuYmtLQkLbwi0Gp2ht0SIrV4AMCL7x2D1e7G3qONcLi8mD0lCx6vGNIMZElKogZe0beVry+iKKK20YbURC2aWk/PY7bY3dDrVINiIUhvGJCJiIgGqcbGRgCIy5v06urqkJ2d3edrYlXtbjM7YNSrghpZJi3gaLc44Q1xfJqkpWPM3FVzC9BqduK1D0uxdX8dsocYMCI3Ed6wA3LH3GZr32e1OTxwON0w6FSwOz2wO3091k6XFwadKuSbBAeCgb8sm4iIiHokBeTOPch6vR4ajSbmFeSamhpMmTKlz9fEKsy3W1wYlmmEWh14QJaWhZg7ZiErw+g+aDH5qrUTC1MhQsR7X1QCABbNHwlBEODximGNWEv1LwvxwO3xQqPo+bDN7fZONwr6WivQcdOiUafyPzcYsYJMREQ0SPVUQRYEIebb9DweD+rr6/utIMeqxcJkdcFoUAfVQjA0Ve/7Wosr7G16Le2+1ocEgxqXnDcMo4YnQ61S4NyJvh903B4xrIUcqUm+MG+xOXvdpuf1iqhvtsHYsQxEq1aiuc0Bk8V3s55hkLdYsIJMREQ0SDU0NADoGpCB2G2okzQ0NMDr9QbcYhHNgGxzuOFye5GgV0MVRAvB0BRfi4VJhm160ipno14NhULA/9wwAS3tDv8aZ68o+tskQiG1g1hsnl636ZltLrjdXhyuasepFhvmzchFq9npb+0w6IP7AWKgYUAmIiIapHpqsQB8ldlYBuSamhoA8K+/7k1SUhKA6PYgt5lOV2+DGaNm0KuhUSvQbnHCE2YFudXkgFaj9IdRjVqJzHQDAMBmdyMlQdttzXMw0joqyGZ779XuhhYbVEoBb37yLVraHZgzNRsiRJxqtgEADDrloA7IbLEgIiIapBobG/0tFZ3FaoWzpLa2FgD6rSCrVCokJiZG9azN7b7+3wR98C0ESUYNzJbwZyG3mn1TNFrNjm7P2Z0eZA0xhPX+GrUSWo0SJouzx2q32+NFc5sD1acsaGz1LQkpPdEGhSCgvsUXkPVa9iATERHRANTQ0IDU1FSoVF2rjbGuIAcakIHor5tubvOFUqNBHfSUhuQEDUxWF5wBzBfuS3tHQFYqFDBZT49ic7m90GqVSDQEvsCkN0lGNcxWF1w9bNOz2NwQRRE7D56CTuurZH9T0QK9VoXGjnFviQZ1wFsGByIGZCIiokHqzCUhkljfpCe1WGRlZfX72mhXu1tMHQE5hB7b1EQNzDYXnAFsqOtLm9kJo06F9GQtBAj+ldAWuws5Q4yyVG6TE3yrsR09hHmnyzfSbd/RRswYPxSjhiXjm4oWqFUKWKwuCILvB4jBjAGZiIhokDpzzbRECp3hLrQIVW1tLdLT06HV9n+jWbRvKGzp6EFOTdQGXSFNTdLBbHXB0UNVNhgmi6+CbNSrMGp4Miw2N9weLwQISEsO/ea8zlIStbD0shrb4fLgYFkLXG4vvjM5C+NHpqKuyYbmNjtsDjd02uBmRA9EDMhERESDVENDQ68VZI/HA5PJFINT+QJyIO0VQPQryG0mB1RKAQn64G+CS0/RwebwwGJzhfz5oij6xszpVVAplUg0ajA8KwGNrXZkpRugCmPFdGepiVqYbD2HebvDgz1HGpA71ID87ASMG5ECAPjmeCucLi+MOlVYq64HgsH93REREZ3FemuxiPU2vdra2n4nWEii3Q5S32zzrZkOYkmIRFoW0mpyhFydtzk8cHtEJOhP90BnphuQn52IoWm6kN6zt7PaHR5Ybe5uz1VUt+NEnRmzJmdBEARkDzEgOUGDbypaYHO4odcqw9rkNxAM7u+OiIjoLCWKYq8tFtJUi1jdqFdTUxNUBTla52xpt6O5zY5EgzqkgOwfn2Z1hzwLWZqBnGA4PUVDoRCQn50InUa+6bzSspA2s6PbuultX9dBqRAwc2IGAN9ymfEjUnG0ohVmm2/1dDib/AaCwf3dERERnaXa29vhcrl6bbEAYlNB9nq9qKurCyogt7e3w+MJr6+3P26PFxU1ZtgcHhj1amjVwUekISm+bXrt1tC36bVIY+YMmqCnaARDWhZisrq7nNXu9GDfN42YMjodCR034lntbowdkQKL3Y3qejP0WhVUDMhEREQ00PS0ZloitVjEooLc1NQEt9sdcIuFdNb29vYIngqoabDC4/HCYnPBoFNBHUIFeWiqL3Sarb2vcO5Pc7s0RcM35i1S/NVum6vLNr3KGhMsdjemjEoH4PvBwWJzYUROIgDAKwJ6nSqoJSoD0eD+7oiIiM5SfQXkWLZYSCPeAq0gS2dtbm6O2JnMVhdqGyxI7JgNbDQEvyQE8N2kBwDtFhc83tAqyK0dY+Z8c5AjV0EektJzmK9vtgIA0lN8AdpidyM9RQeNWolhWQkAfEtCBvMWPYABmYiISDabN29GfX19rI8BoPc100Bsb9ILZkkIAAwbNgwAcOLECdnPYne6UX3K3LEEQwm32wuHy4sEnRqqENobVEoFjHpVrxvqAtHaMWYuyaiJ6KY6KSCbLO4uYb6+Y5V0aqIvIHs9IrI61lyP75hmoR/ka6aBCAfkgoICTJo0CVOnTkVxcTEA30+ACxYswKhRo7BgwQL/T6+iKOK+++5DUVERJk+ejH379kXyaERERLIqLy/HhRdeiL///e+xPgoA34g3oOcKcnJyMgRBiEkFWQrIgbZYFBQUAAAqKipkO4Pb40XZyVYcKGlGTaMVCQY1dFoVzFbfeDbfFr3QIlJSggbmMLbptZqcUCoEf/9vpOi0Kui1SpisXcP8qWYbBPgWidgdbiQlaJCcoIFapcDo4ckAAKMu+CUqA03EK8ifffYZ9u/fjz179gAAli9fjvnz56O0tBTz58/H8uXLAQAbNmxAaWkpSktLsXLlSvzoRz+K9NGIiIhk8+STT/onR8SDvlosFAoFkpOTB0wFWaFQyBqQbXY3mlodSE5QI9mo8Ye9LfvrAACZafqQA2BKx7rpnjbUBaK1Y820JgqLOBKN3cN8Q4sNiUbfDwg2pwdZ6QYIgoDUJC3yMhPw/ctHYfyIlIhWt+NB1Fss1q5diyVLlgAAlixZgnfffdf/+G233QZBEHDeeeehtbXV/y8RERFRPGttbcXzzz/v/9/xoLGxERqNBomJiT0+H+0NdZKamhqkpKRApwtspq9arcawYcNkriCLEBTosimvorodH+04iVmTMzE8OyGkFgvANz7NFOA2PVEUcbLe7F8lDQDtZgcM+ugs4kg2qmG2urts02tssSMlUQuvV4RSoUCS0VfJTknUwOsV8Z0pWdBplRGdsBEP5Buo1wNBELBw4UIIgoB77rkHS5cuRX19vf+nxqysLH+vVnV1tb/PCADy8vJQXV3d7SfMlStXYuXKlQCAuro6f7M/dSX91RqFh9dRHryO8uB1lEckruMzzzwDi8WC1NRUnDp1Ki7+bKqsrERaWlqvxaaEhATU1taGfNZQr2NFRQWGDh0a1Ofm5OTg2LFjsl3XFpMd7S02eO2+GORye/H82kokGVSYPy0BbS2NqKtzB71qGgB0Ko/vpr+6WiRqbH2+1mpzofJkLZKNGiQlaAAATS0WaJVetLc0oEZpCf6bC4JeI6Kx1YbamhqoRd8PUqdaLMhM1aKmpgbJiVrU1/vaTtweEW2tLfA4fDcy1od4fSJF7n+vIxqQt27ditzcXJw6dQoLFizA2LFjuzwvCELQF3fp0qVYunQpAKC4uDjgHqazEa+NPHgd5cHrKA9eR3nIeR1dLhdefPFFzJs3DyqVCm1tbXHx62S1WpGRkdHrWYYOHQq73R7WWUP52paWFgwfPjyorx07diw++ugj2a6r95QZDq/N3+f71qZv0djmwn03TURWVgocTg9yc7u3pgRiWLYJ7r2NUGpT+j1veVUbkpPTOl6bAgCwOo8gI9WAnNwc/81xkZI1tB7HTlqRkDIUOTlpEEUR7ZavMGV0EoxJQzCmKA0J+tO90GaXHm63FxqDiNzc7jd/xpqc/95FtH6fm5sLAMjIyMC1116LXbt2ITMz0//TbG1tLTIyMvyvPXnypP9rq6qq/F9PREQUr9566y1UVVXhgQceQEpKSly1WPTUfyxJTU2NWYtFoP3HkhEjRqCmpgZ2u12WMzhdHigVAjweL3YerMenu6oxd1o2xo1IhcfjDWuNsjRfuN3cdb5wT2doarMj0ahBi8npX9Zhsrqgj9KmurRkHRxODyw2X5W4zeyEy+1FklEDnVYFo07V7fUWhxtqVfxUjiMlYlffYrHAZDL5//fHH3+MiRMn4qqrrsLq1asBAKtXr8bVV18NALjqqqvw0ksvQRRF7Ny5E8nJyUH/C0RERBRNoihixYoVGDNmDC677LK4CsgNDQ09jniTpKamRv2soiiitrY26D/fpUkWlZWVspzjVIsdH39ZhV8/vRur3y9BzlADrp03AgDg9YphBWRpFrLJ6oSnj2160jg3hQCIEGGx+bbv2exuJOjVUenxlcJ8a7tv3XRto6+lI8GgwpAUbbe/5U80qOFxi1ApI38DYaxFrMWivr4e1157LQDA7XbjlltuwaWXXooZM2bghhtuwKpVq5Cfn4///ve/AIDLL78c69evR1FREQwGA1544YVIHY2IiEgW27Ztw549e/DMM89AoVDEVUDur4Ici5v0Wlpa4HQ6g/6r8BEjfOG1oqICY8aMCesMbo8Xf/j3XtgcHowfmYpbLi3CxMI0/1QGj1eENowJEqcXcLjh9ojQ9DCtTRRF1DZaYdCqYLYDGpUCTW12/7i1RKM6olv0JOnJXbfp1TfZOj7fV0E+k16rglarjEp1O9YiFpBHjhyJr7/+utvj6enp2LRpU7fHBUHAU089FanjEBERye6TTz6BIAj4/ve/D8A3X9jhcMButwc8pSES3G43Wlpa+m2xsNlscDgc0Gq1UTlXsFv0JJ0Dcrha2h2wOTxYfPFIzJvRvZXT5fFCE8KaacnQFD0AX6uEu5cKssXuht3hhtnmQlWNBTOnZKC5zQlvR0BO0EdnzvDparfvrNKSkGSjBqoepmgoFALSkrRQxtHNeZEy+H8EICIiipCqqipkZmbCaDQCOL2hrq2tLYanApqamgD0vEVPEottesHOQJZkZ2dDq9XKEpAbW3whUKr0duZye6FSKHp8LlDJib65yu19bNNrbLFBpRTw0gcl+M+mWnhFER6vFzWNvjXPxii3WJisvgpyQ4sNCsFXQe6tzSQtSQdtD9XlwYYBmYiIKERVVVXIy8vz/3MsVzh31teSEElqaioARLXNItgtehKFQoH8/HxZAnJDi+9GvySjpttzJqsLBTmJYfUgC4KARKMaJosTrh5mIbs9XjS02NHYZkdVvQUOlxelJ9qgVipQfcrXA2zUqaJSQR7SUe02W13wdFSQk4waKASh1znQqUlaZKXrI362WGNAJiIiClF1dXWXiUsDMSAPhAoy4GuzOH78eNhnaGzrCMgJXQOyyerEkBQtUpPCbzdJSdD2uk3PYnPBK4rYcaAeapUCaqWAA6XN0GuV/vBuNIa+6joYWo0SBp3KX+1ubLUjJUkLCOixxUIST/OPI4UBmYiIKES9VZBj3WIhLU3o7yY9ILoV5JqaGiQmJvpbUoJRUFAgSwW5ud0BAP4NcYCvqiuKwPCsRFnCX0qSBiaLC05X9x5kp8sDl8uL3YcbMH3cEBTmGnCgrAkKhQCz1TduLbmH6nakJBnVsFjdcLq8aGl3IDVRA41KMehXSfeHAZmIiCgEFosFra2tXSrIycnJAOKngtzfmDcg+i0WoS5zGDFiBJqamvwjZEPV1GaHQafyV0gdTg/azE7kZyeGdXNeZ2lJOphtPa+bttk9OFTeDLvTgzlTszA234jmNgdqGqywOdzQa5XQhTFFI1hJCZqOarcbLe0OJCdooNUO/jFu/WFAJiIiCkF1dTUAxHUPcnp6eq+vidVNeqHuOJBrkkVLmwOJBjVsDjdaTA5AAMbkp4R1Y96ZhiRrYbG5YbW7uj1nc7qx6/ApZKXrMTI3CWOGJwAADpQ1weHyIMGgDqsHOlipCVqYrS40tvjGzCUnaKCX6QeFgYwBmYiIKARSQI7HHuSGhgYkJib2Ob4tFhXkULboSeQKyM3tDhg7VkyPLUjFxMI0pCXrZO2rTesI260mJ7xnbNOrqDahstaMOVOzIQgCkgwq5Gcl4GBpM0xWF4x6tWyV7ECkJvkCcl2Tb4JGklFzVkyp6A8DMhERUQiqqqoAdK0gGwwGqFSqmAfkxsbGPtsrAECr1UKv10ftrNIWvXBaLIDwA3Kb2YlEgxppyTokJ2gicsNZWrIvILdbuq6bFkUR276ug0opYObEDN9jACaNSsPxGhPqm2ww6lTQqqMXz9JStHC6vajrWBKSlKAJa1HKYMGATEREFIKeKsiCICAlJSXmN+n1t0VPEs1teu3t7bDZbCFXkNPT02E0GsOaZCGKItrMTiTo1dBpIheBhqZ0bKizOrssC7HYXPjqaCOmjE5HgkENryiiuc2OcSNSIcIX3g06FVTRbLFI9J216pQZAJCcoIE6CjOY4x0DMhERUQiqqqqQkpLSbSJDcnJyzCvIDQ0NAQXk1NTUqAXk0tJSAKGNeAN8P3yMGDEirAqyxe6Gy+1FklENlTJyVdKhqR3b9CxueDotC6moMcHm8GDaGN+vjdXmRoJRg/RkLVITfZMrjHpVnyPW5Dako9p9st4CpUKI+ufHK14BIiKiEJw54k2SkpIS84BcVVXVpbLdm2ie9X//93+RmJiIBQsWhPwe4Qbk5tbTM5AjualOuuGv/YwKcl3HpjzpebdHRHqSFqIoYGJRGoDobdGTpHVUu2saLL7rohCiWsGOV7wCREQ0IDQ0NMDtdsf6GH5nLgmRxDogW61WNDQ0ID8/v9/XRquCvHXrVrzzzjt48MEH++2N7osUkEWx5xXO/ZGWhCQa1BGtkqpVShj1KrSbnXC5TwdkaZV0erIOTpcHWo0SqUlaKJUCJhb6AnKCXh2VLXqS9I4KstPl9a2e7mOL3tmEAZmIiOLWkSNH8Ic//AHTpk1DRkYGnnzyyVgfyS9eK8gnT54EAAwfPrzf16ampkb8rKIo4uc//zlycnKwbNmysN5rxIgRMJvNaGpqCunrG1p8N6IlGtURD4HJCRqYbS44O81Crm+0Qq1SwKhXwepwI3uIAYIgID1Zi4LsRNy4oBDjRqZEtYKcnqyD9GkpiRpo1cqzYlNefxiQiYgoLr344ouYMGECfvvb38JgMCAhIQHffPNNrI8FAHC5XKivr++1ghzLm/QqKysBIKAKcjRu0nv77bexc+dOPPbYYzAYDGG9V7CTLDxnjFhravNt0UvQayK+yjklUQuTpeuykPpmG9KStRD9r/H1HacmaeEVRVxYnAOdRgmlInrxTKNWwqD3jXVLTtBEdUlJPGNAJiKiuPTll18iJSUF1dXV2LZtG8aMGYPa2tpYHwuAb+GFKIo9VpBjfZNeMAE5NTUVbW1t8Hi6b3yTg8vlwkMPPYQJEybg9ttvD/v9CgoKAAQekCuq2+Fwnv7emtvsUCoEJOiVEW9jSEvS+jbUdVo33dhqR3qyDja7G0OS9f55x0a9GoAAURQhiohqiwXgm30s/X+OePNhQCYiorhUVlaG0aNH++fmZmdno6amJsan8ulpi54kJSUFFosFLlf3LWrRcOLECSgUioDmDWdmZkIURTQ0NETkLC+88ALKysrw5z//GUoZpkYEW0G22d0wWZ3+f25udyDBoIZGE/lFGGnJvgUcDoevb14URTS1O5Ce7Js7nJGm979WpVQgOVEDq90NpVIBRbQDcoIvICca1awgd2BAJiKiuFRWVoaioiL/P2dnZ8dNBVlaEtJbiwWAmLVZVFZWIjc3F2q1ut/XSgFf+n7k9sUXX2D48OG47LLLZHm/pKQkJCYmBvz7wOMV0dgxuQIAmtt8ATkaizjSk3XweEW0mp3++cs2uxspiVroNL6b+Lq+XgubwxPVNdMSacRccoImqlv84hkDMhERxR2Hw4ETJ050C8inTp2Ki0kW/VWQgdgG5EDaK4DT55e+H7mVlJRg7Nixst70lZmZifr6+oBf32Y6PWqtxeRAokENbRRCYLp/m55v3XRtxwSLRIMaQ1O6r7ZONPhCqiaKW/Qk0rKQ5ARNTAJ6POJVICKiuHP8+HF4vd4uATknJweiKAYVjiKlqqoKOp0Oqamp3Z6TAnKs+pBPnDgR0AQL4HQFPBIVZFEUUVJSgtGjR8v6vhkZGTh16lTAr/eIIsxWX7tLm8mJBL0qKn220qxjs8UNt0dETYMvICcnaKDTdm/x0GqUSDSqYxKQiycMxaSiNCTo1VCpOMECACLfhENERBSksrIyAEBhYaH/MWkDW21tbUBLMCKpuroaeXl5PVZGk5OTAcQmIHs8HlRVVQVcQc7IyIBKpYpIQD516hTa29tlD8iZmZkoKSkJ+PUalQJNbXYkGtQwW11IMKqhiWJAbrf4Kti1jRYAvpv3eqvSDknRw+P19vhcJE0bMwQGrRoQRKi5RQ8AK8hERBSHysvLAaBbiwWAuOhD7mtTXSwryDU1NXC73QEHZOlmvki0WEghNlYV5BN1JnxT0QK9VoWWdiea2hwQASQZtVGZEpHRsW7aF5BF1DVZoVIKMBjUvW6qG5Ki8wfraFIqFBAhQiEIER9/N1DwKhARUdwpKytDYmJil61r0lSGeJhk0duSECC2AfnEiRMAAlsSIsnLy4tIBTmSAbmxsbHf0XTPrz2K/24sB0QRHq8XVad8FdwkY2S36EmMHTcDmqwueDxenGqyITVJC4Xgq2r3RK1SQBeFCRtnUioFeDwiR7x1woBMRERxR5pg0bmFITMzE4IgxLyC7PV6UVNT029AjsVNesHMQJbk5uZGrIKs0WiCCuuBkEbTNTY29vm6C6bnoN3iwrfV7VApBZysMwHwBeRobapLStB0LAvxoqHVjrQkLZQKRdxVaVVKBQQA+hiE83gVX79CRERE6D7iDQBUKhWGDh0a84Dc2NgIp9PZa4tFQkICBEEYcBVkURT7f3EQSkpKUFhYKMv8484yMjIAoN82i/MmZkKtUmDf0UYYtCpUd9wkl6DXRKWCDPhuyDNZXXA43WhusyMtWReXc4aVCgEKpQCtNv7OFisMyEREFFfcbjcqKiq6BWTA12YR6xaLvka8Ab6+3lht06usrERaWhoSEhIC/pq8vDxYrVbZK96RmGAB+CrIAPqdZqLXqTBuRAq+KmmCIAhot/gWhiQYVFBFqYKcmqSFyeJEq8kJs82NlAQNdHEYQhUKoaO9I/7OFisMyEREFFdOnDgBt9vdY0COh2UhfS0JkaSkpMQsIAfTXgFEZtSbx+Pxb0KUW6AVZACYMiodJosLZVVtsFhd0GmV0OtUss5l7kt6sg5mqwv1TadHvBl6GPEWD/RaVUxGzMUrXgkiIoor0oi3eA3I/VWQgdgF5BMnTgQdkCOxLOTEiRNwOp0xrSADwLiRqVCrFNj7TSPsTg+SjZqoLAmRpCfr4HB5UdNoAwAkJ2jj9kY4o04VtdaTgYBXgoiI4kpPI94kOTk5qKur63eCQSRVVVVBqVT6g1pPUlJSon6TniiKqKysDPqmuEhUkCM1wQLwXVu1Wh1QBVmrVmJiYSq+OtqIlvbobdGTpCX7NtSdqGvv+Of43VSXm2GEUd//evKzRXz+KhER0VmrrKwMer3eP/e4s+zsbHi9XjQ0NMTgZD7V1dXIzs7u8+azWPQgt7S0wGw2B11BlsbnDZSALAhCUNv0zhk7BCarC8drTVHfVCfNNK6sNUOhEJBg0PQ6AznW4m2yRqzxahARUVzpacSbJB6WhfQ1A1kSixYLaYJFsAFZo9EgIyND1haLkpISJCYm9lllD0dGRkZALRZKpYBxI1KhUSsgioBRH50tehIpIFefsiA1UQOFovcZyBRf+KtERERxpaysrMuK6c6kgBzLSRbV1dX+qmtvYhGQpRnIocwdlntZiDTBIlI3w2VmZgZUQR6SrIPbK2JSURoAIMGgjmpAlbbpuT0i0pN1cTkDmXrGXyUiIoobXq8X5eXlPfYfA6fbAWJZQT516hSysrL6fE1KSgpMJhO8Xm+UThXakhBJXl6e7BXkSLRXSHqqIJtMJtx2223Yvn27/7HkRC1Er4hzxvo2MiYa1FENqCmJWig61lqnJmk5Rm0AYUAmIqK4UV1dDYfD0WtAloJprAKy2+1GU1OTf9RYb1JSUiCKItrb26N0Ml+LhU6n67KeO1C5ubmyVZDtdjsqKysjHpBPnTrVZblJaWkpXn755S6/N/RaJbQaJcaPSME1FxZg/IjUqM1ABnzzhZMMvhvf4nUGMvWMAZmIiOJGXyPeAF+/bHp6esxaLKT1xv0F5OTkZACIapuFNMEilLaGvLw8NDc3w2azhX2O8vJyiKIY0YCcmZkJu90Ok8nkf6y0tBQAMGrUKP9jgiAgI80Ah9uLS2YNgz4Go8ySEzX+/x+vM5CpOwZkIiKKG/0FZMDXZhGrCrLU9xpIBRmIfkAOpb0COD3qTY42i0hOsJD0tCxECshn/t5JSdBA7Oh0ESFGtYIMAKmJvlFvycb4nYFM3TEgExFR3CgvL4dGo+lzSkQsl4VIfa/xGJBDWRIikXNZiBSQO1dy5dbTspDS0lLk5ubCYDB0ea1ep4JWo4TT5YEgCFG/SS41yReQU5O1cTsDmbrjrxQREcWNsrIyjBw5ss8Zw9nZ2TFrsQi2ghytZSF2ux319fUhTbAA5F0WUlJSgszMTH+bSST0VEEuKyvrNZRnpOlhsrliMmJtWFYCdFolkozquJ2BTN2xGYaIiOJGXyPeJNI2Pa/XC4UiuoEjXlssTp48CSC0CRaA/AE5ku0VwOkK8pktFldffXWPr09J0MDrRVRnIEuuPH848jISoFQKnIE8gPBXioiI4kZNTU2/Sziys7P90ySi7dSpU1Cr1f4A3Jto36R3/PhxAKHNQAaAxMREJCUlydZiEemALE3qkFos2tra0NDQ0GsFWa9TIUGvhjaKW/QkWo0KRr2KM5AHGP5KERFRXHC73WhsbOx3xnAsl4WcOnUKGRkZ/U6KSEpKAhC9gLxz504IgoDJkyeH/B7BLAtpbW3tMkFC0tTUhFOnTmHMmDEhnyMQarUaaWlp/gpyTxMszpSRpodOHf0KskopwOMVOQN5gGFAJiKiuNDQ0ABRFAMOyLG4UU8KyP1RqVRITEyMWg/y559/jilTpiAtLS3k9wh0WYjb7cbs2bNx7733dntu9+7dAIDp06eHfI5AdV4WIk0/6TMgp+owNM3Q6/ORolIqoBDACRYDDAMyERHFhbq6OgDoNyDHcpteoAEZiN66aYfDge3bt+Oiiy4K630CXRbyyiuv4MiRI9ixYwdcLleX56IdkM+sII8cObLX1yuViphMkVAqBCgVChh1vO1rIOn3d8o//vEPtLS0ROMsREQUJW+++SbGjRsn63rhcAUakAdCBRmIXkD+8ssvYbfbceGFF4b1Pnl5eairq4Pb7e71NU6nE48++ij0ej1sNhv27dvX5fndu3dj7NixEZ1gIcnMzPRXkEtLS5GXl9dtxFs8EAQBGo2CFeQBpt+AXF9fjxkzZuCGG27Ahx9+2GWtIxERDUwbN27E0aNHsXjxYjidzlgfB8DpgCxNKOiNTqdDSkpK1HuQRVFEfX19wAE5OTk5KgH5888/hyAIOP/888N6n9zcXHi9Xv+vQ0+ee+45HD9+HM888wwAYMuWLf7nRFHErl27MGPGjLDOEagzK8iRnLscLqNOxRnIA0y/v1p/+MMfUFpairvuugsvvvgiRo0ahV/96lcoLy+PxvmIiCgCjh49itTUVOzYsQM/+9nPYn0cAIEHZCA22/QsFgtsNlvcVZA///xzTJ06FampqWG9T3/LQmw2G/7whz9gzpw5uO222zBy5Ehs3rzZ/3xVVZW/qBYNmZmZaGlpgdPpjPuAPDRVDz1bLAaUgH6cEQQBWVlZyMrKgkqlQktLCxYtWoRf/OIXkT4fERFFwLFjx3Dddddh2bJl+Mc//oHXX3891kdCfX09kpKSAvpr8lhs05OqlYEEeMAXkOW+Sc/r9Xb5Z7vdLkv/MdD/LOSnn34atbW1ePzxxyEIAmbOnImtW7f6zyT1H5977rlhnyUQ0g8qpaWlaGpqiuuAnJKohYoj3gaUfn+1nnzySUyfPh2/+MUvMHv2bBw8eBDPPPMM9u7dizVr1kTjjEREJKOWlhb/KK4//elPmDNnDu6++24cOXIkpueqq6vrt/9YEotteoEuCZHIXUE+deoUUlNT8Z///Mf/2JdffgmHwxF2/zFweoayNFO5M5PJhOXLl2PBggWYO3cuAGDmzJloaWnB4cOHAfgCskqlwpQpU8I+SyCkH1S2bdsGACgqKorK59LZod+A3NzcjLfffhsfffQRFi9eDLVa7ftChQLvv/9+vx/g8Xgwbdo0XHnllQCAiooKzJw5E0VFRbjxxhv9vW8OhwM33ngjioqKMHPmzB7/BSUiovAdO3YMADB27Fio1Wr897//hcfjwYsvvhjTcwUTkKUWi2jeFxNqQD6z6huqkpIStLe34yc/+Qmam5sB+NorFApF2P3HAJCWlobU1NQeWyg///xzNDY24sEHH/Q/NnPmTACn+5B3796NyZMnQ6fThX2WQEi/DlJAjucKMg08/QbkRx99tNfVlePGjev3A5588skur3vwwQexbNkylJWVITU1FatWrQIArFq1CqmpqSgrK8OyZcu6/EtIRETyOXr0KAD4lzlkZ2ejoKAg5oWJurq6gNsXcnJy4HQ6o7pNL9iALN30JlcriPT5DQ0N/j8jP/vsM0ybNq3fzX6BKiws7DEgS3OGp06d6n9s2LBhyM3NxZYtW+D1erF79+6otVcApyvIW7duhSAI/a4oJwpGRBtiqqqq8MEHH+Duu+8G4LvD9dNPP8WiRYsAAEuWLMG7774LAFi7di2WLFkCAFi0aBE2bdrEiRlERBFw7NgxqNVqjBgxwv9Yfn5+zANyfX19wBVkqV82mmPqpIAqrTnujzST99tvv5Xl86WRZt/73vfw3HPPYePGjdi5c6cs7RWSwsJCfxjurKysDCkpKV0WkQiCgLlz52Lz5s0oLS1Fe3t71G7QA07/oPLtt99i2LBhUatc09khordU3n///fjzn//sX0fZ1NSElJQUqFS+j+28tae6uhrDhg3zHUqlQnJyMpqamjBkyJAu77ly5UqsXLkSgK/aEItVowNBQ0NDrI8wKPA6yoPXUR5yXcf9+/ejoKCgy/sNHToUe/fujdl/U+12O1pbW2EwGAI6g0ajAQAcOHAg4MAqCfU6fvvtt0hMTPS3N/THaDQCAPbs2SNLdVOq7P7617/G559/jkWLFsHhcGDSpEmy/bplZmaisrISlZWV/pZKADh8+DDy8/O7VMMbGhowadIkvP7663juuecA+H7QitbvIVEUodPpYLfbMWzYsAGbB/jfR3nIfR0jFpDff/99ZGRkYPr06fj8889le9+lS5di6dKlAIDi4mL/RiXqjtdGHryO8uB1lIcc17GyshITJkzo8l4TJkzAq6++ipSUlJgsW6isrAQAjB49OqDvUfqrfrvdHtI1CeVrLBYLMjMzA/7aoUOHQqFQoKWlRZZfN6vVivT0dIwdOxb/+te/cOWVV0KhUOCaa66RbTHH1KlT4fF44Ha7u7RXnjx5Eueee2637+O73/0ufvWrX+Gll16CwWDABRdc4C+CRYMU6CdOnDig/xszkM8eT+S8jhFrsdi2bRvee+89FBQU4KabbsKnn36Kn/70p2htbfVv6amqqvL/NVlubi5OnjwJwLfnva2tDenp6ZE6HhHRWcnlcqGsrAxjx47t8rgUhqSgGm2BbtGTZGdnQxCEqLdYBNp/DABqtRrDhw+XrcWi8+dfccUVuO2223DppZfKurVOmgTRuc3C6XTi+PHjPU6JGD9+PNLS0nDq1ClMnz49quEYON1mwRv0SG4RC8h//OMfUVVVhePHj+ONN97AvHnz8Oqrr+Kiiy7CW2+9BQBYvXo1rr76agDAVVddhdWrVwMA3nrrLcybNw+CIETqeEREZ6WKigq4XC7/DXqSgoICAD2P+IqGYJaEAL7wmZGREfWAHOj5JCNHjpRtsVZ9fX2Xz3/xxRfxwQcfyPLeEqkVpPOZKysr4fV6ewzICoUCc+bMAYCo9h9LpOvBgExyi/rU6j/96U9YsWIFioqK0NTUhLvuugsAcNddd6GpqQlFRUVYsWIFli9fHu2jERENep1HvHUW64As3YAWaAUZ8P3NYzxXkAFf4IxEBRlARIpI2dnZ0Ov1XSrI0v/ubc6wNGIuFgGZFWSKlKj8XciFF17ov8t25MiR2LVrV7fX6HQ6vPnmm9E4DhHRWevMEW+SrKwsaDSamLdYBBNAc3Nzo3Zej8eDxsbGoAPyyJEjcerUKZjNZiQkJIR1hjMryJEgjUvrXEHuLyAvXrwYn376KRYsWBDRs/Vk2LBh0Gg0XSayEMmBew+JiM4ix44dQ0ZGBlJTU7s8rlAoMHz48Ji2WAwZMqTL5IT+RLOC3NzcDK/XG1JABsIf9eZwONDW1hb054eiqKioWwU5ISGh18/Oz8/H+vXrY3Lf0P33349t27ZxxBvJjgGZiOgscvTo0W7VY0ksl4UEs0VPkpubi6amJtjt9gid6jSpBSSUFgsg/IAszWCOdAUZON0WIm0ALCsrQ2FhYVzeF5SSkoLi4uJYH4MGIQZkIqKzyLFjx7r1H0vy8/Nj2mIRbPiTpiBFY/5tsFv0JHJVkEP9/FAUFhbCbrf7Zx6XlZX12l5BNFgxIBMRnSWamprQ2NjYa0AuKChAXV0dbDZblE8W3BY9SV5eHgDfyNBICzWgpqamIiUlJexJFlIFOxoV5M6j3jweDyoqKhiQ6azDgExEdJaQJlj01WIBACdOnIjWkQD4NqKF2mIBRGfddDgtDiNHjhxwFWTAN+rt5MmTcLlcDMh01mFAJiI6S0gTLPqqIAPRXxZiNpthtVrjPiArlcpuNzcGQo5Rb9GsIA8fPhwqlQplZWX9TrAgGqwYkImIzhLHjh2DRqPxB+EzSdv0on2jXrBLQiRJSUkwGo1RC8jS6uhgjRw5EhUVFfB4PGF9vsFggNFoDPk9AqVSqVBQUIDy8nIGZDprMSATEZ0ljh49ilGjRkGpVPb4fE5ODlQqVcwCcrAVZEEQojbqLZQlIZKRI0fC5XKFdc5ozEDurKioyB+QdTodcnJyovbZRPGAAZmI6CxRUlKC0aNH9/q8UqnE8OHDo95iEcoWPUm0AnJ9fX3IAVmOUW/hBPRQFBYWoqysDKWlpSgsLAypck40kPF3PBHRWUAURZw4caLX9gpJfn7+gKkgA9ELyOFWkAGENcki2hXkwsJCtLW1YdeuXWyvoLMSAzIR0VmgtbUVVqvVPxqtN7FYFlJXVwelUhnSJrbc3FzU1NT4l1pESjgBediwYVCpVAOqgiyF4rq6OgZkOisxIBMRnQVOnjwJwBfW+lJQUICamho4HI5oHAuAL4RlZGSE9Nf4ubm5cLlcaGxsjMDJfKxWK8xmc8gVXJVKhfz8/JADstfrRUNDQ9QryBIGZDobMSATEZ0FpGUa/VWQpUkWUqCOhlCWhEiiMeqtoaEBQHgziEeOHBlyi0VzczM8Hk9UK8gjR470r5ZmQKazEQMyEZHMvvrqK7z22muxPkYXwVSQgeiOegtlSYgkGgFZjiUd4SwLieYMZIlOp/Nf287VZKKzBQMyEZHM/vKXv+C2227zB5t4UFVVBYVC0W8QPVsDsiiKeP3112G1Wrs9J20WDDcgNzU1oa2tLeivjeYWvc6KioqgVqv7/aGKaDBiQCYiktnx48fh8XjwxhtvxPoofidPnvTPOe5Lbm4ulEpl1Ea9eb3esFossrKyoFAowg7I+/fvxy233IK//OUv3Z579dVXMXToUEyZMiXk9w9m1FtFRYW/JQaITQUZAC666CJceOGF/f6eIRqMGJCJiGQmhcuXXnopxic5raqqqt/+Y8B3Q1leXl7UKsgtLS1wuVxh3QCXmZkZdkCWNsY99dRTsNvt/sdramrw3nvv4Y477oBWqw35/YMZ9fbd734X119/vf+fY1VBfvjhh/Hxxx9H9TOJ4gUDMhGRjBwOB2pra5GdnY19+/bh0KFDsT4SAF8FOdC/Ko/EqLfepmKEsyREIscsZOn7bWhowKuvvup//Pnnn4fH48EPfvCDsN5/xIgRXT6nNydPnsThw4exa9cuf2ivr6+HUqlEWlpaWGcgosAxIBMRyejkyZMQRRHLli2DSqXCyy+/HOsjQRTFgCvIgG+ShZwtFidPnkRSUhL+9a9/dXuutrYWQOwDckVFBVJTUzF16lSsWLECoijC4/Hg3//+N+bPnx/2JIeUlBSkpKSgoqKiz9dt3LjR/7+lFp1Tp05h6NCh3GZHFEX8t42ISEZSsJwxYwYuu+wyvPLKK/B4PDE9U0tLC6xWa1AV5OrqajidTlk+/+jRo3A6nbjvvvuwc+dO/+NWqxW/+tWvoNfrMXbs2JDfPzc3t0vPbigqKiowYsQIPPDAAzhy5Ag++ugjfPTRRzhx4gTuueeesN5bMmLEiH4ryBs3bkRWVhbmzJnjD8jR3qJHRAzIRESykgJQQUEBbrvtNtTU1ODTTz+N6ZkCnYEsKSgogNfrDTt0SmpqagAASUlJWLRoEU6dOgWv14vbbrsNu3fvxmuvvRZWAMzLy/NvCgzV8ePHMWLECNx4443Izs7GihUr8OyzzyIjIwNXX311yO/bWUFBQZ8VZK/Xi08++QQXX3wxbr75Zhw+fBgHDx6M+hY9ImJAJiKSVWVlJRQKBXJzc3HllVciJSUl5jfrBToDWSItC5GrD1kKyOvWrUNTUxNuuukm/PznP8eaNWvwxBNP4Jprrgnr/cMd9SaKIo4fP46CggJoNBr8z//8DzZu3Ij3338fd955JzQaTVjnk0gVZFEUe3x+//79aGxsxMKFC7Fo0SIolUq88cYbrCATxQADMhGRjI4fP468vDyo1WrodDrceOONePvtt2E2m2N2plAqyABk60Ourq5GSkoKZs2ahWeeeQafffYZVqxYgR//+Me4//77w37/cANyXV0d7Ha7/0a6e+65B3q9Hl6vN+yb8zorKCiAzWbzT6U4k9R/fPHFFyMjIwPz58/3B2RWkImiiwGZiEhGlZWV/gosAHz/+9+H1WrF+++/H7MznTx5EkqlEtnZ2QG9Pi8vDwqFQtYKck5ODgDg9ttvx29+8xvceeed+Nvf/uZfZxwOKSCH2hIitT1IATk9PR2PPPIIfvKTn/jHs8mhv0kWGzduxKRJk/y/TjfffDO+/fZb2Gw2VpCJoowBmYhIRtJf1UtmzpwJrVaLvXv3xuxMVVVVyM7OhlKpDOj1Go0Gubm5EQnIAPD73/8eq1atkm0BRUFBAQRBCGjGcE86941LHnzwQfz973+X4XSnSe/fUx+y1WrFli1bsGDBAv9j1157rb+9gxVkouhiQCYikonb7UZ1dXWXCrJKpcL48eNx4MCBmJ0rmBnIEjlHvZ0ZkOWm0+mQn5+PkpKSkL5eCqydA3Ik9BWQt2zZAqfT2SUgJycn4/LLLwcQ/S16RGc7BmQiIplUVVXB4/F0C1qTJ0/GwYMHY3MoBL5FrzO5loV4vV7U1tb62yAiZfTo0QEFZLvd3mVTHuALrJmZmTAYDJE6HgAgISEBQ4YM6fG6bty4ERqNBnPnzu3y+O233w4AsrZ6EFH/GJCJiGQiVVw7V5ABX0Cura1FY2Nj1M8kimJIFeSCggJUVVXB7XaH9fkNDQ1wu90RrSADpwNybxMiRFHESy+9hNzcXPz0pz/t8pw0AzkaRowY0WMF+eOPP8acOXO6hfSrr74aJ06cwJgxY6JyPiLyYUAmIpJJT72sADBp0iQAiEkVuaWlBTabLegKcn5+PjweT9gb6qQRb9EIyO3t7T1OiKioqMAll1yCJUuWwOFw4JNPPoHNZvM/f2bfeCT1VJmvq6vDwYMHu7RXdBbsDzdEFD4GZCIimUgV5DMDzeTJkwEgJn3Iwc5AlkiBMdw2i2gGZADd2ixKS0sxceJE7NixA0899RT+85//wG63Y/PmzQAAj8eDEydORLWCXFlZCa/X63/siy++AADMnz8/Kmcgov4xIBMRyaSyshLZ2dnQarVdHs/MzMTQoUNjEpCDnYEsGSwB+aOPPoLVasWXX36Je++9F/PmzYNOp8OHH34IAP42kmgF5IKCAjidTtTW1vof27FjB/R6PaZOnRqVMxBR/xiQiYhk0tdf1cfqRr1QK8jS68OdZCEF5EBnMIdq+PDh0Gg03QLyvn37kJGRgXHjxgEA9Ho9zjvvPGzYsAFA720xkSIF8c59yNu3b8eMGTOgVqujcgYi6h8DMhGRTM5cEtLZpEmTcOjQIXg8nqieqaqqCkqlEllZWUF9nVarRU5OjiwV5IyMjIiHP6VSiaKioh4D8jnnnNNlIclFF12EY8eOoaKiotuSkEg7szJvs9nw1VdfYdasWVH5fCIKDAMyEZEMvF4vTpw40WcF2Waz4dtvv43quU6ePImcnJyAl4R0Jseot+rq6oi3V0jOHPXmcDhw+PBhTJs2rcvrLrzwQgC+9ouKigoIgoDhw4dH5YzSD1BSMN+7dy/cbje+853vROXziSgwDMhERDKora2Fy+Xqs4IMRP9GvVBmIEvy8/NlqSBHMyCXlZX5q/SHDh2C2+3GOeec0+V1hYWFGDFiBDZs2ICKigrk5eX5N9ZFml6vR1ZWlv+67tixAwBw3nnnReXziSgwDMhERDLor5d1/PjxUCgUUe9DDmUGsqSgoAAnT54Mqy0k2gHZ6XTixIkTAHztFQC6BWRBEHDppZdi06ZNKCkpiVr/saTzLOQdO3agsLCQq6SJ4gwDMhGRDHpbEiIxGAwoKiqKagVZFMWwKsgFBQVwu93+G+2C5XK5cOrUqYhv0ZOcOcli3759SE5O7rG/+LLLLoPFYsGXX34Ztf5jidS6IooiduzYwf5jojjEgExEJAOpgtxbQAZ8fcjRDMjNzc2w2WwhV5Cl7yXUNov6+nqIohjVCjLQNSBPmzatyw16kosuush/42C0A/KIESNw4sQJfPvtt6irq2NAJopDDMhERDKorKzE0KFDu60K7mzSpEn49ttvYTabo3KmUGcgS6TWg1BHvUlb+KIVkDMyMpCUlISSkhK43W4cOHCgW3uFJCEhAeeffz6A6I14kxQUFMDj8eDNN98EAN6gRxSHGJCJiGQQyLriyZMnQxRFHD58WNbPPnjwIJxOZ7fHpW1xoQZkabJDqBXkaC0JkQiCgFGjRqGkpARHjx6F3W7vNSADvjYLIDYVZAB47bXXYDQaMXHixKh+PhH1jwGZiEgGfc1Alkgrp+W8Ue/QoUOYPHkyiouLsWvXLgC+3uPHHnsM9913H77zne/0GRL7otfrkZmZGXBAttvtcDgc/n+OdkAGTo966+0Gvc7uuOMOPPTQQ1FvcZB+kDp48CDOPfdcqFSqqH4+EfWPAZmIKExerzeggFxQUACj0ShrH7IUtquqqjBr1iw88MADuOmmm/DII4/gtttuw6effhrWCLOCgoKAWixMJhMmT56M66+/3v9YTU0NlEplVCc0jB49GpWVldi+fTsMBoO/L7kn6enp+OMf/xi1EW+S4cOH+/ui2X9MFJ8YkImIwlRbWwu73Y6ioqI+X6dQKDBp0iRZK8ilpaUQBAFHjx7FPffcg7/+9a9488038Ze//AUvvvgitFptWO8f6LKQZcuWobS0FB988AG+/vprAL6AnJ2dDYUien/UjB49GqIo4u2338aUKVNCWpASaRqNxj/ZgwGZKD4xIBMRhamsrAyAbwFFfyZOnChrQC4pKcGwYcOQkZGBp59+Gl9++SU2b96Mn/3sZz1ObwhWfn4+Kisr4fV6e33Ne++9h1WrVuHee++F0WjEX//6VwDR3aInkSrGDQ0NIbeWRIPUh8wFIUTxiQGZiChM5eXlAAILyOPGjUNTUxMaGxtl+ezS0lKMGjXK/8/nnnsu5syZI8t7A74KssvlQm1tbY/Pnzp1CnfffTemTp2Kv/71r7jzzjvx2muvoba2NqpLQiSdr0U8B+QZM2bg3HPPxZAhQ2J9FCLqAQMyEVGYysvLoVKp/FMf+jJu3DgAwNGjR8P+XFEUUVJS0iUUyq2vUW+iKGLp0qVoa2vDyy+/DI1Gg5/+9Kdwu9146qmnYhKQk5OTkZmZCSC+A/Jf/vIXbN26NdbHIKJeMCATEYWprKwMBQUFAU0jGDt2LADgm2++Cftzm5qa0Nra2ueNaOGSAvK3337b7bkvvvgCa9euxeOPP+4fVVZYWIhrrrkGTz/9NFpaWqK2Ra+z0aNHQ61WY/z48VH/7EApFAr/ohIiij8RC8h2ux3nnnsupkyZggkTJuCRRx4BAFRUVGDmzJkoKirCjTfe6J/d6XA4cOONN6KoqAgzZ84Mee4mEVG0lZeX93uDniQ/Px96vV6WgFxaWgoAEa0gFxYWQq1W49ChQ92e27NnDwDfuLTOli1bhpaWFgDRHfEmWbRoEW6//faoT6cgosEjYgFZq9Xi008/xddff439+/fjww8/xM6dO/Hggw9i2bJlKCsrQ2pqKlatWgUAWLVqFVJTU1FWVoZly5bhwQcfjNTRiGgAe/bZZ/Hd734XLpcr1kcB4GszKCsrC6j/GPBVDseMGSNLi0U0ArJGo8G4ceN6vLHw0KFDyM7ORnp6epfH58yZg+LiYgCxCcj33XcfVq5cGfXPJaLBI2IBWRAEJCQkAABcLhdcLhcEQcCnn36KRYsWAQCWLFmCd999FwCwdu1aLFmyBIDvp/9NmzZBFMVIHY+IBqi3334b77//Pn7/+9/H+igAgObmZrS1tQUckAFfm4VcFWSlUhnxTXCTJk3qcXbzoUOHetwCJwgCfvnLX0KpVGLMmDERPRsRUSREdH2Px+PB9OnTUVZWhh//+McoLCxESkqKv08vLy8P1dXVAHzjgIYNG+Y7lEqF5ORkNDU1dbvDd+XKlf7KQF1dnX9TE3XV0NAQ6yMMCryO8pDzOh46dAhKpRKPP/44ZsyYgenTp8v23qGQNralpqYG/N+jvLw8VFZWory8HHq9PuDPOvM6fv311xg2bJhsEzF6U1BQgFdffRVHjhxBSkoKAN9/3w8fPozvf//7PX7f5513Hg4fPgy1Wh13/53mv9fy4HWUB6+jPOS+jhENyEqlEvv370drayuuvfZaWf5KcenSpVi6dCkAoLi4OCZ/fTdQ8NrIg9dRHnJcR7PZjJqaGvziF7/Af/7zHzzwwAPYv38/jEajDCcMzeeffw7AN14t0O9xxowZEEURJpMpqMoz0PU6VlVVYezYsRH/PTp79mwAQGNjo//Gt7KyMtjtdsyaNWtA/jsyEM8cj3gd5cHrKA85r2NUplikpKTgoosuwo4dO9Da2gq32w3A9x936Q7n3NxcnDx5EgDgdrvR1tbWra+NiM5ux44dAwDMnDkTq1evRnl5OX7+85/H9ExlZWUQBAEjR44M+GukUW/htFmIothtBnKkTJ48GQC6tFlIN+311GJBRDTQRSwgNzQ0oLW1FQBgs9mwceNGjBs3DhdddBHeeustAMDq1atx9dVXAwCuuuoqrF69GgDw1ltvYd68ebJsgSKiwUMKlOPGjcMFF1yABx54AM8880xM58mWl5cjNzcXOp0u4K8ZNWoUFApFWH+rVldXB7PZHNERb5KcnBykpaV1uVFPCsjxPEqNiChUEQvItbW1uOiiizB58mTMmDEDCxYswJVXXok//elPWLFiBYqKitDU1IS77roLAHDXXXehqakJRUVFWLFiBZYvXx6poxHRAHX06FEolUp/W8Kjjz4KhUKBjRs3xuxM5eXlQbdJ6HQ6jBgxIqwKcjQmWEgEQeh2o96hQ4cwcuTImLa3EBFFSsR6kCdPnoyvvvqq2+MjR47Erl27uj2u0+nw5ptvRuo4RDQIfPPNNygqKvLPtzUajRg9ejS+/vrrmJ2prKwMV155ZdBfN27cuLAqyNEMyIDvv+kvvPACvF4vFAoFDh48yPYKIhq0uEmPiAaMb775xr+JTjJlypQeR5BFg9lsRn19fdAVZMA36q2kpAQejyekzy4pKYFarUZ+fn5IXx+sSZMmwWw24/jx43A4HCgpKWFAJqJBiwGZiAYEl8uFsrIy/w1uksmTJ6OiogLt7e1RP1N5eTkABLxFr7Nx48bB4XCgoqIipM8uLS1FYWEhlEplSF8frM436pWUlMDtdjMgE9GgxYBMRAPCt99+C5fL1WMFGUBMqshSQA6lgiwF/UDaLFpbW3H99dfjhRde8D8WrQkWkgkTJkAQBBw8eJATLIho0GNAJqIBQQqSZ1aQpYAciz7ksrIyAKEFZCno93ejXmNjI+bNm4e3334bv/nNb/Dyyy/D6/WirKwsqgE5ISEBI0eOxIEDB3Do0CGoVCpuySOiQSuii0KIiOQiBckzK8i5ublIS0uLSUAuLy/HkCFDkJycHPTXpqamIjMzs88Kcl1dHS6++GKUl5dj7dq1+Mtf/oI777wTTqcTdrs9KiPeOps8eTIOHjwIp9OJMWPG+G+WJCIabBiQiWhA+Oabb5CTk4OkpKQujwuCgClTpsQsIIdSPZaMGzeu1wpyfX09LrjgAlRVVeGDDz7AvHnzMGbMGNx44424++67AURvgoVk0qRJWLt2LUwmE84///yofjYRUTSxxYKIBoSjR492a6+QTJkyBQcPHgx5IkSoysrKQrpBTzJ27Fh88803EEWx23OvvPIKSkpK8OGHH2LevHkAgMTERKxfvx7Dhw8HEP2APHnyZHi9XtTU1LD/mIgGNQZkIop7oij2OOJNMmXKFNhsNn9PcDQ4HA6cPHky7Apya2srTp061e25Y8eOYciQId0qtTk5Odi0aROefPJJ5OXlhfzZoZg0aZL/fzMgE9FgxoBMRL0ymUw9Vjejrba2FiaTqc8KMhDdG/WOHz8Or9cbVgVZ+n6OHDnS7bnS0tJee4yLiopw3333QRCEkD87FIWFhdDr9QAYkIlocGNAJqIebdq0Cenp6Vi3bl2sj+Lv0+0tII8bNw5KpTKqo97CGfEmGT9+PICeA3JJSUnUb8Lrj1KpxMSJE6HX6zFixIhYH4eIKGJ4kx4RdXPy5EncdNNNcLlc2LZtG6666qqYnqe3CRYSnU6HsWPHRrSC3Nraig8++AC7du3C4cOH/Z8VTgU5JycHycnJOHz4cJfHzWYzampqot5jHIibbroJR48ejdqCEiKiWGBAJqIuHA4HFi1aBIfDgby8vJitce7s6NGjSEpKQnZ2dq+vmTJlCrZs2SLr57pcLqxevRpvvfUWNm3aBLfbDaPRiPHjx+OKK67A3LlzkZGREfL7C4KACRMm+BdvSKRe6nirIAPAAw88EOsjEBFFHAMyEXWxbNky7Nq1C2vWrME777yDzz77LNZH8t+g11fP7ZQpU/Daa6+hubkZaWlpsnzuunXr8IMf/ACFhYV44IEHcN1112HGjBlQKOTrTpswYQLWrFkDURT9319JSQmA6E+pICIiH/YgE5HfG2+8gWeeeQY///nPcd1112Hy5Mmorq5Gc3NzTM/V14g3SSRWTh84cAAKhQIHDx7En/70J8ycOVPWcAz4AnJzczPq6+v9j5WWlgIIr32DiIhCx4BMRH7//Oc/MWHCBPzv//4vgNNjvQ4ePBizMzU0NAQ0dzcSkyyOHDmCkSNH+ic3RMKECRMAoEsfcklJCfLy8mA0GiP2uURE1DsGZCICADQ3N2PHjh24/vrroVL5uq8mT54MQN6qbLB2794NAJgxY0afr8vKykJGRoasAfnw4cP+SROR0ltAZnsFEVHsMCATEQBg48aN8Hq9uOyyy/yPZWdnIz09PaYV5N27d0MQBJxzzjn9vlbOldMulwslJSURD8hZWVlITU3tEpD7moFMRESRx4BMRACA9evXIz09vUulVhAETJo0KaYV5F27dmHcuHFITEzs97XTpk3DoUOH4HQ6w/7csrIyuN1uf4U3UgRBwMSJE/0BuampCU1NTQzIREQxxIBMRPB6vdiwYQMuueSSbvNtJ0+ejEOHDsHr9Ub9XKIoYvfu3Tj33HMDev306dPhdDq7jU0LhbS8I9IVZMDXZnH48GGIoui/QY8tFkREscOATETYt28fGhoaurRXSCZNmgSLxYKKioqon+vEiRNoaGjot/9YUlxcDADYs2dP2J99+PBhCILQ63ISOU2YMAGtra2ora31B2RWkImIYocBmYiwfv16CIKASy65pNtzsbxRL9Ab9CQjRoxAamoq9u7dG/ZnHzlyBCNGjIDBYAj7vfrT+Ua9kpISKBQKrnImIoohBmQiwoYNGzBjxgwMHTq023MTJkyAIAgxuVFv9+7dUKvV/pDeH+lmPjkqyEeOHIlKewXQPSCPGDECGo0mKp9NRETdMSATneUaGxvx5Zdf4vLLL+/xeaPRiMLCwphVkKdMmQKtVhvw1xQXF+PgwYNwOBwhf67b7caxY8eiFpAzMjIwZMgQHD58mBMsiIjiAAMy0Vnu448/hiiKPfYfSyZNmhT1CrLX68WePXsCbq+QTJ8+HS6XK6wb9crLy+F0OiM+waKzCRMm4NChQ5yBTEQUBxiQic5y69evx5AhQ/w3uPVk8uTJKC0thdVqjdq5jh07BpPJFPAEC4kcN+pFc4KFZMKECdizZw8sFgsryEREMcaATBQDoihi8+bNsszrDYfT6cSHH36ISy+9FApF7/85mDx5MkRR7LLMItKCvUFPUlBQEPSNeocPH0Zra2uXfwYQlQkWkgkTJsDtdgPgBAsiolhjQCaKgQ8++AAXXHABXnvttZie47///S+amppw88039/m6SZMmAUBU2yx2794No9EYdEgVBAHFxcUBV5Bramowffp0LFy4EC6XC4CvglxQUICEhISgzx2qzu0cbLEgIootBmSiKBNFEQ8//DAAeeb1hnOOFStWYNy4cbj00kv7fO3IkSNhMBiieqPe7t27MX369G6LSwIxffp0HDp0CHa7vd/XPvHEE3A6ndi9ezcef/xxANGdYCGRArJWq8WwYcOi+tlERNQVAzJRlL377rv46quvoNPpsH///pid44svvsBXX32FZcuW9dleAQBKpRITJkyIWgXZ6XTiq6++Crq9QlJcXAyXy9XveZuamvCvf/0Lt956K2699Vb84Q9/wI4dO3D06NGoB+QhQ4YgIyMDhYWFIf1QQERE8mFAJooir9eLhx9+GKNHj8Ydd9yBr7/+OiIrnHfv3o2kpCSMGTMG11xzDX75y192C4srVqzAkCFD8L3vfS+g95w2bRr27t0r23ltNhv+85//+PtuOzt48CCcTmfIAXn69OkA0G8f8pNPPgmr1Ypf/vKX+Oc//4ns7Gxce+21cDgcUZ1gIbnllltw/fXXR/1ziYioKwZkoih68803cejQIfzud79DcXExzGYzysvLZf+cbdu2wWQyYezYsSgpKcH//d//Yfbs2di+fTsAoKSkBOvWrcO9994LvV4f0Ht+5zvfQVtbm2w36q1btw433XQTfvWrX3V77sMPPwSAoCdYSPLz85GWltZnC0t7ezv+8Y9/4LrrrsP48eORkpKCF198EfX19QCiO8FC8te//hWPPfZY1D+XiIi6YkAmihKPx4Pf/e53GD9+PG644QZMnToVACLSZlFWVoaUlBS8++67OHLkCI4fP46srCxccskl2L59O/72t79Bo9Hg3nvvDfg958yZA8AXvuVQUVEBAPjLX/6CNWvW+B//+OOP8cgjj+Dyyy9HQUFBSO8t3ajXVwX56aefRmtra5eAPn/+fDzwwAMwGo0xCchERBQfGJCJouT111/H0aNH8eijj/p7elUqFb766ivZP6usrAxFRUUQBAEAkJubi88++wzZ2dm45JJL8OKLL+J73/seMjMzA37PkSNHIjMzU7aAXFlZieTkZMycORN33HEHjh07hkOHDmHRokWYMGECXn/9df/5Q9HXjXpWqxUrVqzAJZdc4m/HkPzf//0fKisrozrBgoiI4gsDMlEUuN1uPProo5gyZQquu+46AL5pBePHj49YBbmoqKjLY7m5ufj888+Rk5MDm82G+++/P6j3FAQBc+bMwdatW2U544kTJzBixAi8+eab0Gq1uPbaa3HFFVcgMTERH3zwAZKSksJ6/+LiYrjd7h6v7xtvvIGGhgb88pe/7PacIAhIT08P67OJiGhgY0AmioKXXnoJZWVlePTRR7tMjJg6darsAdnlcuH48ePdAjIA5OTkYNu2bdi8ebN/tnEwZs+ejePHj6Ompibsc1ZWViI/Px/Dhg3DG2+8gWPHjqGpqQnr1q1DXl5e2O//ne98B0DPLSGfffYZMjIyMHfu3LA/h4iIBh8GZKIIczqd+P3vf4/i4mJcddVVXZ6bNm0aamtr/TeGyaGyshIej6fHgAz4xomdf/75Ib337NmzAYTfhyyKIiorKzF8+HAAvt7f9957D5s2bcI555wT1ntLsrKyUFRUhM2bN3d7btu2bZgzZ05YLRxERDR4MSATRdgLL7yA48eP47HHHusWyCJxo15ZWRkA9BqQwzFt2jTo9fqw2yza2tpgMpmQn5/vf+yKK67AzJkzwz1iF3PnzsXWrVu7jKarqalBRUWFP+wTERGdiQGZKILsdjv+8Ic/YNasWT1uq5syZQqAgROQ1Wo1Zs6cGXYFubKyEgC6BORIOP/889Hc3IxvvvnG/5h0dmkqBxER0ZkYkIki6LnnnkNVVVWP1WMASE1NRX5+vuwBOSEhARkZGbK9Z2ezZ8/G/v37YTabQ34PKSBLLRaRIrWSdG6z2LZtG/R6PaZNmxbRzyYiooGLAZkoQrxeL/74xz9i7ty5mD9/fq+vmzZtmqyj3s4c8Sa3OXPmwOPx4Msvvwz5PU6cOAEg8hXkkSNHIicnB1u2bPE/tnXrVpx77rlQq9UR/WwiIhq4GJCJIkSa9vC9732vz7A6depUlJSUwGKxyPK5paWlEWmvkMyaNQuCIITVZlFZWQmtVouhQ4fKeLLuBEHA+eefj82bN0MURZjNZuzfv5/tFURE1CcGZKIIOXDgAABg8uTJfb5u6tSpEEURBw8eDPsz3W43KioqIhqQk5OTMWnSpLAD8vDhw7uMvIuU888/H9XV1Th+/Dh27doFj8fDG/SIiKhPDMhEEXLw4EEIgoAJEyb0+TqpF1aONouTJ0/C5XJFNCADvj7kHTt2wOPxhPT1J06ciHh7hUSadbxlyxZs3boVgiBg1qxZUflsIiIamBiQiSLkwIEDGDlyZL8ri4cNG4bU1FRZbtSL5ASLzmbPng2TyRRy1bvzDORImzBhAlJTU7FlyxZs27YNEydOREpKSlQ+m4iIBiYGZKIIOXjwYL/tFYCvT3bq1KnYu3dv2J8ZrYAszSves2dP0F/rcDhQV1cXtQqyQqHAnDlz8Nlnn2HHjh1sryAion4xIBNFgNVqRWlpaUABGQDmzZuHvXv3orq6OqzPLSsrg16vR3Z2dljv05/CwkIkJyeHFOpPnjwJIPITLDo7//zzUV5eDpPJxBv0iIioXwzIRBFw5MgReL1eTJo0KaDXL168GACwZs2asD63rKwMhYWFEb/5TRAETJ8+PaAKssPh6LLJLlozkDvrvFqbFWQiIupPxP4UPXnyJC666CKMHz8eEyZMwJNPPgkAaG5uxoIFCzBq1CgsWLAALS0tAABRFHHfffehqKgIkydPxr59+yJ1NKKIk3pzA60gjxkzBhMnTsRbb70V1udKM5CjYfr06Thw4ACcTmePz588eRL33XcfUlJS8Je//MX/eLRmIHd2zjnnwGAwIDc3N6qfS0REA1PEArJKpcITTzyBI0eOYOfOnXjqqadw5MgRLF++HPPnz0dpaSnmz5+P5cuXAwA2bNiA0tJSlJaWYuXKlfjRj34UqaMRRdyBAweg1+sxcuTIgL9m8eLF2Lp1K2pra0P6TK/Xi/Ly8qgF5OLiYjidThw6dKjL421tbfjBD36AwsJCPPPMMxg6dChefvllOBwOAL4KsiAIyMvLi8o5AUCj0WDJkiVYsmRJxBaoEBHR4BGxgJydnY1zzjkHAJCYmIhx48ahuroaa9euxZIlSwAAS5YswbvvvgsAWLt2LW677TYIgoDzzjsPra2tIQcFOvtUVlbinXfe8f/fxx9/DFEUY3aegwcPYuLEiVAqlQF/zeLFiyGKYshtFtXV1XA4HFGtIAPo1of8f//3f1i1ahWWLl2KsrIyPPfcc2hpacF7770HwPdrlZ2dDY1GE5VzSp5++mk8/vjjUf1MIiIamFTR+JDjx4/jq6++wsyZM1FfX++/gSgrKwv19fUAfH+4Dxs2zP81eXl5qK6u7naz0cqVK7Fy5UoAQF1dHWpqaqLxLQw4DQ0NsT5CVF1zzTXdxqT9+9//xuWXXx7W+4ZyHUVRxFdffYVLLrkkqN+fycnJGDNmDF599VVcd911QX/uzp07AQApKSlR+fdCp9MhOTkZX3zxBa644gr/42vXrsWMGTPwq1/9CgAwfvx4ZGdn46mnnsLs2bNRWlqK7Oxs/rsbgrPt3+tI4XWUB6+jPHgd5SH3dYx4QDabzbj++uvxt7/9DUlJSV2eEwQh6L/uXLp0KZYuXQrA91e8OTk5sp11sDlbrk1dXR3279+PBx54ALfddhtEUcQ111yD119/HXfffXfY7x/sdayrq0NzczNmzpwZ9NfefPPNePTRRyEIQp+TKFwuFzZv3oz33nsPTU1NAHw/iALAeeedF7Vf++LiYhw9etT/eXV1dTh48CAef/zxLme45ZZbsGLFCjidTtTV1WHGjBlnze9PufG6yYPXUR68jvLgdZSHnNcxore6u1wuXH/99bj11lv9FbHMzEx/60RtbS0yMjIAALm5uf7xTwBQVVWF3NzcSB6PBokPP/wQAPD9738fU6ZMwdSpU3HPPffg008/xTfffBP18wR7g15nixYtgiiKeOedd3p8vrGxEXfeeSeysrJw8cUX49///jd27tyJnTt3oq6uDgsWLIhqb690o57UXyz9WpxZub/xxhsBAKtWrcLJkyejOsGCiIgoWBELyKIo4q677sK4cePwwAMP+B+/6qqrsHr1agDA6tWrcfXVV/sff+mllyCKInbu3Ink5OSIz3KlwWHDhg3Izs7GlClT/I/ddddd0Gg0+Ne//iX7523ZsgVFRUX49ttve3z+wIEDABDwiLfOJkyYgHHjxuHNN9/s8fk///nPeOmll3D55ZfjnXfeQWNjI8rKyvz/9/HHHwfV9xyu4uJiuFwu/416Pf1aAL4fgBcuXIh//vOfcDqdnCRBRERxLWIBedu2bXj55Zfx6aefYurUqZg6dSrWr1+Phx56CBs3bsSoUaPwySef4KGHHgLgqziNHDkSRUVF+MEPfoCnn346UkejQcTtduPjjz/GZZdd1qVdJyMjA4sXL8aLL74Is9ks62c+9thjKC8vx6OPPtrj8wcPHkR2djaGDBkS0vsvXrwYmzdv9vfnd/bRRx9h7ty5ePnll3HNNdfAYDCE9Bly6XyjXm+/FpK7774bra2tAKI74o2IiChYEQvIc+bMgSiKOHDgAPbv34/9+/fj8ssvR3p6OjZt2oTS0lJ88sknSEtLA+DrR37qqadQXl6OgwcPori4OFJHo0Fk586daG1txWWXXdbtuXvvvRft7e147bXXZPu8AwcO4JNPPkFeXh5eeeWVHls4Dhw4EFL1WLJ48WJ4vV68/fbbXR6vra3FgQMHsHDhwpDfW24jRoxAamoq9uzZ0+evBeD7WyLphwa2WBARUTzjJj0a0NavXw+lUokFCxZ0e27WrFmYOnUqnnrqKdlGvq1YsQIGgwGbNm2CwWDAww8/3OV5t9uNI0eOhNR/LJkwYQLGjh3brc3ik08+AYC4CsjSRr29e/f2+WsBnJ5FrFarWUEmIqK4xoBMA9qGDRswe/ZsJCcnd3tOEATce++9OHDgALZv3x72Z9XW1uK1117DnXfeidGjR2PZsmV466238NVXX/lfU1paCofDEVZAFgQBixYtwhdffIFTp075H//4448xdOhQTJ06NZxvQ3bFxcU4ePAg1q5d2+uvheT3v/89du7c2W2iDRERUTxhQKYBq6amxt+605tbbrkFSUlJstys989//hNutxv3338/AOD//b//h9TUVPzmN7/xv2bbtm0AQrtBrzOpzUKaZuH1erFx40YsWLAACkV8/Ws7ffp0uFwuHDlypN+503q93r9AiIiIKF7F15+0REGQRor11vMKAEajEddeey3Wr18Pj8cT8mdZLBY888wzuPbaa1FYWAjAt9jjF7/4BdavX4+f/OQnmDhxIn7wgx8gKysL48aNC/mzAF/AHj16tL/N4uDBg6ivr4+r9gqJdKMe0PevBRER0UDBgEwD1vr165Gbm9tvtXbhwoVobm7u0goRrOeffx4tLS1dRhYCwE9+8hP/lri0tDT8/e9/x759+6DVakP+LMDXZrF48WJ89tlnaGhowMcffwwAvfb3xlJBQQHS0tIC+rUgIiIaCKKyapooUJWVlVi3bh1+/OMf97ll0Wq1YuPGjbjhhhv63cZ48cUXA/D18IYyHaWyshK//vWvcdFFF+E73/lOl+eMRiP27t0LALLP7V60aBEef/xxvPPOO/j4448xceLEuNy2JAiCv90k2M2YRERE8YgVZIorTz31FH7yk59gy5Ytvb7GbDbjiiuugMlkwi233NLve2ZkZGDatGn46KOPgj6P1+vF7bffDsBXRe4pAGZnZ0dkqc2UKVNQVFSEl156CVu2bInL9grJr371K/zoRz+K9TGIiIhkwYBMcUWqxva2KKatrQ2XXHIJtmzZgldeeQUXXXRRQO+7cOFCbN++HSaTKajz/Pvf/8bnn3+OJ598EgUFBUF9bbikNott27bB4XDEdUAmIiIaTBiQKW6Iooi9e/dCpVJhzZo1qKur6/J8c3MzLr74YuzevRv/+c9/AqoeSxYuXAi3243PP/884K85dOgQli9fjquvvtpfRY62xYsXAwC0Wi3OP//8mJyBiIjobMOATP1yuVyoqanx/19zc3NEPqe8vBxtbW144IEH4Ha78dxzz/mfE0URN9xwAw4ePIh33nkH119/fVDvPXv2bBgMBv/Nbv1pbm7G9773PSQlJWHlypUx662dOnUqxowZg3nz5sV8rTQREdHZggGZ+nXttdciNzfX/39DhgzB1q1bZf+cPXv2AABuuukmLFy4EM8++yzcbjcA4Nlnn8WmTZvw5JNP4oorrgj6vbVaLS688MKAAvLBgwcxY8YMfPPNN1ixYgUyMjKC/jy5CIKATz/9FC+//HLMzkBERHS2YUCmPrW3t+Ojjz7CNddcg2effRbPPvsssrKy8Mtf/lK29c2SvXv3QqvVYsKECbj33ntRVVWFdevWoaKiAj/72c9w8cUXY+nSpSG//8KFC1FSUoLjx4/3+po333wTs2bNgs1mwxdffIH58+eH/HlyycnJQXp6eqyPQUREdNbgmDfq0yeffAK3241ly5Zh7ty5AAC3240f//jH+Oijj3DppZfK9ll79+7F5MmTodFocMUVV2DYsGH45z//CVEUoVAosGrVqrBaHaSb3DZu3Igf/OAH3Z5/8cUXcccdd2DWrFlYs2YNsrOzUVNTE/LnERER0cDECjL1af369UhOTsasWbP8j919990oKCjAb37zG9mqyF6vF3v37vXPKVapVPjhD3+ITz/9FJ999hmeeOIJDB8+PKzPGDt2LPLy8npts/jXv/6FyZMn47PPPovI2DYiIiIaGBiQqVeiKGLDhg1YsGAB1Gq1/3GNRoNHHnkEe/fuxTvvvCPLZ5WXl6O9vb3L2uK77roLOp0OCxcuxN133x32ZwiCgIULF/qr4p3V19dj165dWLRoUdhb8IiIiGhgY0CmXh04cAA1NTW4/PLLuz33ve99D2PGjMFvf/tbeDyesD9LukGv86a7zMxMHDp0CGvXrpVtisSVV16J1tbWblXkDz74AKIo4rvf/a4sn0NEREQDFwMy9WrDhg0A0GOfsUqlwmOPPYYjR47gjTfeCPuzpBv0xo8f3+XxwsJC6HS6sN9fcsUVV2Do0KFYtWpVl8fXrVuHYcOGYcqUKbJ9FhEREQ1MDMjUq/Xr12PatGm99uMuWrQI48ePx7/+9a+wP2vv3r2YOnVql1aOSNBoNFiyZAnee+891NfXAwDsdjs+/vhjXHnllTGbd0xERETxgwGZetTa2ort27f32F4hUSgUuPzyy7Fr1y7YbLaQP0u6Qa9z/3Ek3XXXXXC73XjppZcAAJ9++imsViuuuuqqqHw+ERERxTcGZOrRxo0b4fF4cNlll/X5urlz58LpdGLXrl0hf1ZZWRlMJlOX/uNIGjt2LGbPno1Vq1ZBFEWsW7cORqMRF154YVQ+n4iIiOIbAzL1aMOGDUhNTcXMmTP7fN2cOXMgCAK2bNkS8mdJN+hFq4IM+EbVHTt2DFu3bsX777+PhQsXytrrTERERAMXAzJ14/V6sWHDBixcuBAqVd+7ZFJTUzFp0iRs3rw5pM9yOBx4/fXXodPput2gF0mLFy9GYmIili1bhqqqKk6vICIiIj8G5Bh77LHHMH36dP//XXDBBaiuro7pmQ4ePIi6urp+2ysk559/PrZv3w6XyxXU51RXV+OCCy7A+++/j9/97nf9hnE5GY1G3Hzzzdi7dy8EQcAVV1wRtc8mIiKi+MaAHEOVlZV47LHH4HK5kJOTg5ycHOzYsQOPPfZYTM/16aefAgDmz58f0Ovnzp0Li8WCr776KuDP2LZtG4qLi3H48GGsWbMGDz74YEhnDYe0fGTmzJnIyMiI+ucTERFRfGJAjqEVK1ZAEAR88MEHWLduHdatW4elS5fi+eefR3l5eczOtWnTJowePRp5eXkBvf78888HgID7kJ999llcdNFFSEhIwM6dO3HdddeFfNZwFBcX4+6778bPfvazmHw+ERERxScG5BhpamrCc889h1tvvRXDhg3zP/7rX/8aarUajz76aEzO5XK58MUXXwRcPQaA7OxsFBUV9duH7HA4cM899+CHP/wh5s+fj127dmHChAnhHjlkgiDg3//+N66//vqYnYGIiIjiDwNyjDz11FOwWq34+c9/3uXx7Oxs/M///A9eeeUVHDlyRNbP/OijjzBv3jwcP36819fs2bMHZrMZ8+bNC+q9586diy1btsDr9fb4fH19PebNm4eVK1fioYcewvvvv4/U1NSgPoOIiIgoGhiQY8BqteIf//gHrrzyyh4rqA8++CASEhLw8MMPy/q5jz/+OD777DPMnTsXpaWlPb5G6j++6KKLgnrvuXPnoqWlpddQ/8gjj2Dv3r34z3/+gz/+8Y9QKpXBHZ6IiIgoShiQY+D5559HY2MjfvGLX/T4fHp6OpYtW4Y1a9Zg3759snxmWVkZtmzZgttuuw02mw1z587tMcxu2rQJU6dORXp6elDvP3fuXADotc3iiy++wMUXX4wbbrgh+MMTERERRREDcpS53W488cQTmDVrFubMmdPr6x544AGkpaXh8ccfl+VzX3rpJSgUCvzv//4vvvjiCwDABRdcgEOHDvlfY7PZsH379qDbKwCgoKAAubm5PQbkxsZGHD16FLNnzw79GyAiIiKKEgbkKHv77bdx/Phx/OIXv4AgCL2+Ljk5GUuWLMG6devQ3Nwc1md6vV6sXr0aCxYsQG5uLsaPH4/NmzdDpVJhyZIl8Hg8AIDt27fD4XAEdYOeRBAEzJ07F5s3b4Yoil2e2759OwD0+QMBERERUbxgQJbJhx9+iAsvvBCvv/66P3CeSRRFPPHEEygqKgpoc9utt94Kl8uFt956K6yzff755zhx4gRuv/12/2OjRo3C3/72N+zbtw/PPvssAF//sUql8o9tC9aFF16I2tpaHD58uMvj27Ztg1qtRnFxccjfAxEREVG0MCDL5He/+x2++OIL3HLLLRg7dixWrVrVbbPcjh07sGvXLtx///0B3aR2zjnnYMyYMXjttdfCOtuLL76I5ORkXH311V0ev+GGGzBv3jz8+te/xqlTp7Bp0yace+65SExMDOlzrrrqKgiCgDVr1nR5fNu2bZg+fTr0en3I3wMRERFRtDAgy+Drr7/Gl19+iSeeeAJr1qxBUlIS7r77btxxxx1d2g3++te/IjU1tUslty+CIODWW2/FF198gZMnT4Z0tvb2drz11lu46aabugVUQRDw1FNPwWKx4N5778Xu3btD6j+WZGVl4fzzz+9S8bbb7di9ezfbK4iIiGjAYECWwcqVK6HVanH77bfjuuuuw549e/Dwww/j1VdfxXPPPQcAqKiowNtvv42lS5fCaDQG/N4333wzAOD1118P6WxvvfUWbDZbr6F87NixeOCBB7BmzRp4vd6Q+o87W7RoEQ4dOoSjR48CAPbu3Qun08kb9IiIiGjAYEAOk8ViwSuvvILFixcjLS0NgK8y+/DDD2PBggX4yU9+gq+//hp///vfoVAo8D//8z9BvX9RURFmzpwZUpuF0+nEn/70J0yYMAEzZ87s9XW/+c1vkJeXB51Oh/POOy/oz+lMWhsttVls3boVAPCd73wnrPclIiIiihYG5DC98cYbaG9vxz333NPlcaVSiVdeeQXp6elYvHgxnnvuOdx4443Iy8sL+jNuvfVWfP31191ufuvPM888g5KSEixfvrzPiRkJCQl4++23sXr1auh0uqDP11lubi5mzZrlb7PYtm0bRo8ejYyMjLDel4iIiChaGJDD9Oyzz2L8+PE9thBkZGTg9ddfR3l5OcxmM5YtWxbSZ9x4441QKpV49dVXA/6alpYWPProo7j44otxxRVX9Pv6GTNmyLbEY9GiRdi/fz9KS0uxfft2tlcQERHRgMKAHIavvvoKu3fvxj333NNrhXbu3Ll47rnn8Itf/ALTp08P6XMyMjKwYMECvPbaa/B6vQF9zV//+le0tbVhxYoVfVaPI+H6668H4Ftt3dTUxBv0iIiIaEBhQA6R3W7HQw89BJ1Oh+9///t9vvaOO+7An/70p7A+7/vf/z4qKyuxadOmbs/9/ve/R05ODpYvX4729nYcO3YMq1evxt13341JkyaF9bmhyM/Px4wZM/DSSy8BACvIRERENKAwIIfAarXi6quvxscff4wnn3wSqampEf/M66+/HkOGDMHTTz/d5XGz2YwnnngCLpcLv/zlL5Gfn49rr70WOp0Ojz32WMTP1ZtFixZBFEUMGTIEo0ePjtk5iIiIiILFgBwkk8mEyy+/HJ988gleeOEFLF26NCqfq9Vqcffdd+O9997rMhP55ZdfRltbG9577z3s3r0bF110Eb755hssW7YMmZmZUTlbT6Q2i9mzZ0e9xYOIiIgoHAzIQWhtbcUll1yCrVu34tVXXw144YdcfvjDH0IURf9qaFEU8fe//x3FxcU477zzUFxcjLfffhtNTU3dpmpEW2FhIX73u9/h/vvvj+k5iIiIiILFgBwEk8mExsZGvPnmm7jpppui/vn5+fm48sor8e9//xtOpxOffPIJjh49ivvuu69LlTYtLS0uqraPPPIILrzwwlgfg4iIiCgoqlgfYCAZNmwYDh06BI1GE7Mz3HvvvVi3bh3WrFmD1157DRkZGbKNZyMiIiIiBuSgxTIcA8DChQtRWFiIRx99FCUlJfjtb38LrVYb0zMRERERDSZssRhgFAoFfvSjH+HYsWNQqVT44Q9/GOsjEREREQ0qEQvId955JzIyMjBx4kT/Y83NzViwYAFGjRqFBQsWoKWlBYDvZrP77rsPRUVFmDx5Mvbt2xepYw0Kd9xxBwwGA2688UZkZ2fH+jhEREREg0rEAvLtt9+ODz/8sMtjy5cvx/z581FaWor58+dj+fLlAIANGzagtLQUpaWlWLlyJX70ox9F6liDQlpaGvbt24ennnoq1kchIiIiGnQiFpDnzp2LtLS0Lo+tXbsWS5YsAQAsWbIE7777rv/x2267DYIg4LzzzkNraytqa2sjdbRBYcyYMUhKSor1MYiIiIgGnajepFdfX+9vCcjKykJ9fT0AoLq6GsOGDfO/Li8vD9XV1T22D6xcuRIrV64EANTV1aGmpiYKJx94GhoaYn2EQYHXUR68jvLgdZQHr6M8eB3lwesoD7mvY8ymWAiCENKs3qVLl/q31xUXFyMnJ0fuow0avDby4HWUB6+jPHgd5cHrKA9eR3nwOspDzusY1SkWmZmZ/taJ2tpaZGRkAAByc3O7rE+uqqpCbm5uNI9GRERERAQgygH5qquuwurVqwEAq1evxtVXX+1//KWXXoIoiti5cyeSk5M5nYGIiIiIYiJiLRY333wzPv/8czQ2NiIvLw+PPvooHnroIdxwww1YtWoV8vPz8d///hcAcPnll2P9+vUoKiqCwWDACy+8EKljERERERH1KWIB+fXXX+/x8U2bNnV7TBAEjiwjIiIiorjATXpERERERJ0wIBMRERERdcKATERERETUCQMyEREREVEnDMhERERERJ0wIBMRERERdcKATERERETUCQMyEREREVEnDMhERERERJ0IoiiKsT5EqIYMGYKCgoJYHyMuNTQ0YOjQobE+xoDH6ygPXkd58DrKg9dRHryO8uB1lEdv1/H48eNobGwM+v0GdECm3hUXF2PPnj2xPsaAx+soD15HefA6yoPXUR68jvLgdZSH3NeRLRZERERERJ0wIBMRERERdcKAPEgtXbo01kcYFHgd5cHrKA9eR3nwOsqD11EevI7ykPs6sgeZiIiIiKgTVpCJiIiIiDphQCYiIiIi6oQBeYC48847kZGRgYkTJ/of+/rrrzFr1ixMmjQJ3/3ud9He3g7AN/NPr9dj6tSpmDp1Kn74wx/6v2bv3r2YNGkSioqKcN999+Fs67CR4zparVZcccUVGDt2LCZMmICHHnooJt9LLMn1+1Fy1VVXdXmvs4Vc19HpdGLp0qUYPXo0xo4dizVr1kT9e4klua7j66+/jkmTJmHy5Mm49NJLQ5qdOpAFcx0B4MCBA5g1axYmTJiASZMmwW63A+CfM3JcR/454yPX70lJUH/WiDQgfPHFF+LevXvFCRMm+B8rLi4WP//8c1EURXHVqlXib37zG1EURbGioqLL6zqbMWOGuGPHDtHr9YqXXnqpuH79+sgfPo7IcR0tFov46aefiqIoig6HQ5wzZw6voxja70dRFMU1a9aIN998c5+vGazkuo4PP/yw+Otf/1oURVH0eDxiQ0NDhE8eX+S4ji6XSxw6dKj/2v385z8XH3nkkcgfPo4Ecx1dLpc4adIkcf/+/aIoimJjY6PodrtFUeSfM3JcR/454yPX70lRDP7PGgbkAeTM/7AnJSWJXq9XFEVRPHHihDhu3LgeXyepqakRx4wZ4//n1157TVy6dGmETx1/wr2OZ7rvvvvElStXRuawcUyO62gymcTZs2eLhw8fPisDsijKcx3z8vJEs9kc+cPGsXCvo9PpFIcMGSIeP35c9Hq94j333CM+++yz0Tl8HAn0On7wwQfirbfe2u3r+eeMT7jX8Uxn658zoijPtQzlzxq2WAxgEyZMwNq1awEAb775Jk6ePOl/rqKiAtOmTcMFF1yALVu2AACqq6uRl5fnf01eXh6qq6uje+g4FOx17Ky1tRXr1q3D/Pnzo3beeBXKdfztb3+L//f//h8MBkPUzxuvgr2Ora2tAHzX8pxzzsHixYtRX18f9XPHm2Cvo1qtxjPPPINJkyYhJycHR44cwV133RWTs8eT3q5jSUkJBEHAJZdcgnPOOQd//vOfAfDPmd4Eex07458zXYVyLUP5s4YBeQB7/vnn8fTTT2P69OkwmUzQaDQAgOzsbJw4cQJfffUVVqxYgVtuuaVLjw51Fep1dLvduPnmm3Hfffdh5MiRsTp+3Aj2Ou7fvx/l5eW49tprY3zy+BLsdXS73aiq+v/t3U1IVG0YxvErEQaGxsRaFOPCHEammaYpdGNRVJsglEA0WrQWyk0LpaCWQUIuhCAXKoTiqugLdJEICYME41c1KCjGRMMsnBZKJZijT4vehvPSK7zHzHPU/281Ko/cz8XAfc/hOWfSOnnypMbHx1VdXa3m5maHd+E8uzmurKyoo6NDExMTymQyOnbsmO7du+fwLpy3Xo65XE7xeFx9fX2Kx+N69uyZhoaGHK7WvTaaI33md3az3GivKfwbxWNrhEIhvXr1StLPT079/f2SJI/HI4/HI0mqrKxUIBDQzMyM/H6/0ul0fn06nZbf79/6wl3Gbo5VVVWSfj6UPBgM6saNG47U7TZ2c0wkEhodHVVZWZlyuZzm5+d19uxZvX792qktuILdHCsrK+X1elVXVydJamhoUHd3tzPFu4jdHM0/N5IFAgFJ0uXLl9Xa2upA5e6yXo6lpaU6c+aMDhw4IEm6ePGixsfHdfXqVfrMf7Cb46+rxfSZ39nNcu/evRvqNVxB3sbm5+clSWtra7p7927+buxsNqvV1VVJ0ocPHzQ7O6vy8nIdOnRIRUVFevPmjYwx6unp0aVLlxyr3y3s5ihJd+7c0eLiotrb2x2p2Y3s5njt2jVlMhmlUinF43FVVFTs+uFYsp/jnj17VFtbm89uaGhI4XDYkdrdxG6Ofr9fU1NTymazkqTBwUEdOXLEmeJdZL0cL1y4oPfv32tpaUm5XE7Dw8MKh8P0mXXYzVGiz6zHbpYb7jV/cnAaW+fKlSvm4MGDprCw0Pj9ftPV1WXa29tNMBg0wWDQ3Lx5M39o/cmTJyYcDptYLGZOnDhhXr58mf8/iUTCRCIRU15ebpqamvJrdovNyPHTp09GkgmFQiYWi5lYLGY6Ozud3NaW26z34y//94bInWazckylUub06dMmGo2a8+fPm48fPzq1JUdsVo4dHR0mFAqZaDRqampqzOfPn53akiPs5GiMMb29vSYcDptIJGJaWlryv6fP/HmO9JmfNus9+YudXsNXTQMAAAAWHLEAAAAALBiQAQAAAAsGZAAAAMCCARkAAACwYEAGAAAALBiQAcDFFhYW9PDhQ0lSJpNRfX29wxUBwM7HY94AwMVSqZRqamqUTCadLgUAdg2+ahoAXOzWrVuam5vT8ePHFQwGNT09rWQyqUePHun58+f69u2bZmdn1dzcrO/fv6u3t1cej0cDAwMqKSnR3NycmpqalM1m5fV61dnZqVAo5PS2AMDVOGIBAC7W2tqqQCCgyclJ3b9//19/SyaTevr0qRKJhG7fvi2v16uJiQlVV1erp6dHktTY2KgHDx5obGxMbW1tun79uhPbAIBthSvIALBNnTt3Tj6fTz6fT/v27VNtba0kKRqN6t27d/r69atGRkbU0NCQX7O8vOxUuQCwbTAgA8A25fF48q8LCgryPxcUFCiXy2ltbU3FxcWanJx0qEIA2J44YgEALubz+fTly5cNrS0qKtLhw4f1+PFjSZIxRm/fvt3M8gBgR2JABgAX279/v06dOqWjR4+qpaXF9vq+vj51d3crFospEonoxYsXf6FKANhZeMwbAAAAYMEVZAAAAMCCARkAAACwYEAGAAAALBiQAQAAAAsGZAAAAMCCARkAAACwYEAGAAAALH4Aete70itKSBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the param and model classes for Prophet model\n",
    "from kats.models.prophet import ProphetModel, ProphetParams\n",
    "\n",
    "# create a model param instance\n",
    "params = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results\n",
    "\n",
    "# create a prophet model instance\n",
    "m = ProphetModel(air_passengers_ts, params)\n",
    "\n",
    "# fit model simply by calling m.fit()\n",
    "m.fit()\n",
    "\n",
    "# make prediction for next 30 month\n",
    "fcst = m.predict(steps=30, freq=\"MS\")\n",
    "\n",
    "# plot to visualize\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Holt-Winters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from kats.models.holtwinters import HoltWintersParams, HoltWintersModel\n",
    "warnings.simplefilter(action='ignore')\n",
    "\n",
    "\n",
    "params = HoltWintersParams(\n",
    "            trend=\"add\",\n",
    "            #damped=False,\n",
    "            seasonal=\"mul\",\n",
    "            seasonal_periods=12,\n",
    "        )\n",
    "m = HoltWintersModel(\n",
    "    data=air_passengers_ts, \n",
    "    params=params)\n",
    "\n",
    "m.fit()\n",
    "fcst = m.predict(steps=30, alpha = 0.1)\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Forecasting with Ensemble model\n",
    "\n",
    "`KatsEnsemble` is an ensemble forecasting model, which means it allows you to combine several different forecasting models when building a forecast.  When creating an ensemble, you specify the list of models (with parameters) that you wish to include in the ensemble, and then you choose whether to aggregate these forecasts using the median or the weighted average.  Prior to building any forecasts, the model checks for seasonality and if seasonality is detected, it performs an STL decomposition (using either additive or multiplicative decomposition, as specified by the user).  Each of the forecasting models specified to the ensemble model are only applied to the the de-seasonalized components, and after these forecasts are aggregated the result is reseasonalized.\n",
    "\n",
    "When we initialize `KatsEnsemble`, we include a dictionary with the following components:  \n",
    "\n",
    "* **models:** `EnsembleParams`, contains a list of parameters for each of the individual model parameters     \n",
    "* **aggregation:** 'str', either 'median' or 'weightedavg', how to aggregate the individual forecasts to build an ensemble      \n",
    "* **seasonality_length:** int, the length of the seasonality of the time series      \n",
    "* **decomposition_method** str, either  'multiplicative' or 'additive', the type of decomposition of the initial time series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example below, we use the `air_passengers` data set to build a median ensemble forecast that combines 6 different forecasting models.  We use the `EnsembleParams` object to define the parameters for each of these models.  Then generating a forecast for this ensemble is straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from kats.models.ensemble.ensemble import EnsembleParams, BaseModelParams\n",
    "from kats.models.ensemble.kats_ensemble import KatsEnsemble\n",
    "from kats.models import (\n",
    "    arima,\n",
    "    holtwinters,\n",
    "    linear_model,\n",
    "    prophet,\n",
    "    quadratic_model,\n",
    "    sarima,\n",
    "    theta,\n",
    ")\n",
    "\n",
    "# we need define params for each individual forecasting model in `EnsembleParams` class\n",
    "# here we include 6 different models\n",
    "model_params = EnsembleParams(\n",
    "            [\n",
    "                BaseModelParams(\"arima\", arima.ARIMAParams(p=1, d=1, q=1)),\n",
    "                BaseModelParams(\n",
    "                    \"sarima\",\n",
    "                    sarima.SARIMAParams(\n",
    "                        p=2,\n",
    "                        d=1,\n",
    "                        q=1,\n",
    "                        trend=\"ct\",\n",
    "                        seasonal_order=(1, 0, 1, 12),\n",
    "                        enforce_invertibility=False,\n",
    "                        enforce_stationarity=False,\n",
    "                    ),\n",
    "                ),\n",
    "                BaseModelParams(\"prophet\", prophet.ProphetParams()),\n",
    "                BaseModelParams(\"linear\", linear_model.LinearModelParams()),\n",
    "                BaseModelParams(\"quadratic\", quadratic_model.QuadraticModelParams()),\n",
    "                BaseModelParams(\"theta\", theta.ThetaParams(m=12)),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "# create `KatsEnsembleParam` with detailed configurations \n",
    "KatsEnsembleParam = {\n",
    "    \"models\": model_params,\n",
    "    \"aggregation\": \"median\",\n",
    "    \"seasonality_length\": 12,\n",
    "    \"decomposition_method\": \"multiplicative\",\n",
    "}\n",
    "\n",
    "# create `KatsEnsemble` model\n",
    "m = KatsEnsemble(\n",
    "    data=air_passengers_ts, \n",
    "    params=KatsEnsembleParam\n",
    "    )\n",
    "\n",
    "# fit and predict\n",
    "m.fit()\n",
    "\n",
    "# predict for the next 30 steps\n",
    "fcst = m.predict(steps=30)\n",
    "\n",
    "# aggregate individual model results\n",
    "m.aggregate()\n",
    "\n",
    "# plot to visualize\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Multivariate Model Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vector autoregression (VAR) is a multivariable forecasting algorithm that is supported in Kats.  Here, we show show an example of how to use the `VARModel` with the `multi_ts` data set.  We begin by loading the data set into a `TimeSeriesData` and previewing it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_df = pd.read_csv(\"../kats/data/multi_ts.csv\", index_col=0)\n",
    "multi_ts = TimeSeriesData(multi_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e7Smo2m1doDnv3ZI9orHDlxZQr2vRtiOfFEB5tep7Vzckpw+kc/7u3hzm5DEeL8Gzfb5bFLdOh9z3a9TH5TstHwtrf3E1ZspVy985m5/nPgRDbZmGnUtc71NBEl4zlS7vppE37g8/3O69+KBASpKrZ9Sgt5sd2rCPm76dn+B7w+9uGPq8f0O9zlCpDvS61QW3u1cHuuUQt8YtZVRQzc91gU5u+UZrx0NKIvS7z//njWzigqiyVVyNp7f6wK8PwxvdXQ/zoGegbOWcjw8Ld+/f0XNOTeJOSwns8J0/+t0DSXtg6ZjCOV9RiFnkcs/8nbQTYhRAebFvmyucmFsvRb0u7oN4zhuhPbW9JIue6+5a/Zl1NvgluHcZ3LPEdZvnV/PzIO2IW6k+VKQmuxyoggzfnaxUWbhqjAsAvr/V5ZUUh0rsoWLrTEhKgLMecikAEaVd9eWNk7xfx1mvuaVEsi682u8+KF0RZrwQ0GaTesRNkqnVtuDPVaaS68VZNtbNssvO6h+gSkOI6u7tuWu3g9tmwaVvQIUabtiufKSbKe1P73KXUa4n6nffjXCmjZPc8F37y721pyAa9XM363kV/vUicTe83q3gixpnZLicsIIsDm0txCyA+h7/j0OIAigvVn3nvrYdkvt+A56A1CSY+XLw2yQnOnrIzTrzt+q2Ma43MbKp92n+WTXqB+FlQqu68O6VLqcjkAEUuLv4a39wMxxnveJ678Q/G/9wNbkaZcnDaX6uq2PkZRJCzCLYNtPl8mV93ZaPhO7Xu7o6J5fpKIj4dWAz3NBSIPT/m+tdmffOqY8d2Q+bpkK7IfnrOQ2PcOVLrv8FntgFf53uf45NWJir0ZS0xwVfmZZ96Yb3CmP4LpMxLlUkdmn+qrdnZ9V3bj3Dn+91vaH5sW8bvN4Vnqzh/n1wnqur5/UGYP82d52j8jFDOEQogPJXRoarON2wb/Yzu7Kq2crVB5r/fuBe+OKfnb6kRH8DqEApXR4a93PTqUNFIBLIc5KZQ1a7vSsHkVNPgpxo4x/QpL/rycvU/DzfY5P8f57Zr0KZKq5m2cl63e7KdORULDY/4nxDgrXaBeb5GvRww2GzXjuxpwfc0F5GKrQNQKmA8Ajvx9Tt5IapF33seliS9sK6ia5obn6eryCOVW8P0HJRq753ZRz2R7tcXa8OxbvZuEf2uo6Cvve4m7TfHoMpT3p7rsyyKOqBOg1smeamwPa4yb/9z3rE3bFNfy6ozZKTZE5jbtCj8M/d5Ew3E+pQfOGfOztb/oRK9VyNlWAIj3B36wdj9Dr3x97Nbhis+fknbq/W2JVFySyp4c/zrPkZetzohsNOVqW++7Bf8hkcTihwswEXQIWXcT21gXLO3+HoQTeJI7MIZnoqLPnCDd/VL8LE4h43uSBj0xTXa5ORWjSFHvNbvT07B3a4xP1et0HHK93izDmVbMjJD7fBwZ1w9deuhtX5/4Fbprre6Bkveps1uGMRRJR1w67FlAIofy38EMpXd/VG/FGtEXS73i35ciguqE2TLLbPhZpt8p8MXhCZwzKhkAeVmgwbp7gZT4FIIM9JQ18y9Nx34EBM8M6TXxkZLoE4+UBRt+T48G7zbJYAaTvEvW78ea+Y86Zbu63XbTnv0+9el7cUqGV4dq+Gmi0D2wNTu51bp3LVd26W3KYp8Pllbmiyz53Bfd3mpdVgN2Q34WGX51e7Q96TUoLhWPX2P/Nf6iLTmp/c13ZD3QhJeoq3xXi3znQ9qOc8AQ2z9PAbAxe/7IY3J/3b/9pV0XNd73hBUieKWMkKoNLTgpPQejAW1v7qVqj3MgOj+w1uOGnNz4Fvk5wqIwO2L8i96F4w1evsZv9tm100589qy3RXRyZzCCCYBjwGWPjz2eCfy4vt8+GDc+HdM+HZJvD55acOFxWmjX+4GkrZFWptN8z1WGd+yOXk8B7XQ9NxhJt1l5Nard0+895z718FFbc6cMN3WfW7D/rc5YbLPhvmPlSHvu0W8y5KEaXdDN29m11B3qs+L7qArtUgb9Xbc7LqBzfkXqOFr3fP+B9AWQuT/+sWT+9x86mPh4XDwP9ByiH/cn8PJ7gUg2Ja/ylTyQqg5r4F75+Tv5oquclc4iO7ZUFyU6utW/Zg1feBbY9kL36tW1OpYe+iOX94KRe8BbIHylqIX+96eL4YAa92Pv7vrT45v9bX/epmYzUphOURqjZ0HzZLvgidBbUPxcGnQ9zabYOed1PCN/8JY67Kf/JsQSTtdR+ArQdn/3itNm64ZtUPuT/P/PfdbM++9+R9zrMfdUNPf/yrYBWtk/ZCYmzgEsizMgYu+D+X6H3DRLh7sStSHArOfMjN0L3y84LXUSuIBr1db9jKb/L/HOmpLj80Mwm+bBWXq+vv8kwbJ7vhv7MehlLlst+nVhs3NDjfj6B981TAHs//K6ZKVgBVJcq9cX5yMbx3tivtP+s196YTtyZ/vVPWwtIvIaqn9yU+jHF3lttmhU5eTEmWWZivsBPIs2rUD3avKvgSDBkZsPxrN+X4zR4w8RE3e6Z+VzfNOqqHq6D+zQ1uavLJx66f6N4sC6tmTf8HXW2fOa8XzvnyMuNFSDsK1/3saiOd92+3hMf2efD19e4DpTDllUdjjFu4ObdhvJQk9+HUcpD78MtLZFPoeSss/wqeawrPNoYXW3tf+DpujfsajB4ocL973U6uLlvVBsE5R36ERxRt4JQpPMLltK2bmP/3lT3rXS9WnY7Ht9Xv7gpZ+vO5uPhjqFATOl+T+34DHnPPN+5a9/6Uk41/QLnI4ExwKUQlK4BqfxncvdB1JYaXdsHTpH+4yslv9XZB1f5ob8+5a7lbfTu/CYTthvrXNS8Ft32em2ESyERXrxr1A6wbisivtKMwZgR8d7MbErz4ZV+tqsUw/EMXCFz+Plz9lZv99v2t7phMm6e6HoNWgwr86/itYi1XI2f510VfhX9/tMtZ7HLNiTc97Ya5pPcNv8GPdx1PXC4My8a64ZPc8mjyeq9Y+oWb/dTPj96nTBc85SqUn/0YtB/uqnF/dyts8FC4M9o3JF0UOUDidLrKBeCZpXS82rXSfa3T/vi2qG6uJtm+Lbkfm3UWYl5V6Ks1hss/cIHZuOuyX64nI8P1aDU7p1iuf5dVyQqgwHUv9rkTbvodnoiFx3bAvcvhohfcePZ7A7ytKr9sbP6X+AA3jFejpfe7PvEueq4bvivK5NP63dxspS3T83d8Rjp8/1c3pf3CZ93QRvcbs78TrtUGLnoeNk+DTy51PReHE+DHO93SMnnVKwu0Hje6vKvlRVyhfMZLgHHDDSfrcZObfr18rFvyI3ZZ8NuzZ6MrGJjXTVjme0V2w3gZ6a44b/3urnq5v8LC3LDy2Y/A4Bfgup/c62bcKNi/3b/nWPWj69WtVNv/80pg1engegBPrpDur90r3PtS9SwLQWeWD4jJIw9q1ffeZiG2vdSVONk4Kfu8yN0r4HBcsR++g5IYQGUVUQbKVHQz4nreAjdPdj9/fpl/Sx0c2edesC0HugJ1+WGMm/GwbZbLZZHgOBTn7qSKcvgOXH2fpme7iQP5GTKe8ZJ7wzr/SZdIG5bHn2jXUa5XKnYZvNQW3u3v7iqHfwSlK+TrV8i3el3dUMzCj4quOnnyARfAdbzCDeln58yHXF7UruUuwfy7W733THuxYpyry9Thitz3y20Yb9X3rjBmv3sKdoNQtoqrJJ+R5l/piT0b3Qde26H5P6cUnDEugIlZ4K3gaqZdK93EgqyzKGu1dT3cO/LoUFg21u2bdfgvL91vcMN9058/8WbSWpj9BmAKtyhpkJTsAOpkNVvCtT+5SsCfDct+1e6spvyfK9/f/28FO2+Xa9win4s+KtjzSM5CIf8pU7thrjaSl55OcD2k0593H1ZehmnaXw63TnVVqSvWdnd/dT282QWKMa63LG6VS1gtCsvHuVUAuudSr80Ylxd1z1I3C2z1j/DuWcErerv2V9drlNusuUzZDeOlp8LUp1wPRGs/y6jkpmpDd338Sfpf7ZsAU9i9mXKqDle4QDw/vVC7V7pSDFmFR7ibnu3zcz4ubi3EzHfJ4V4D94uecz3hX4yAyU/Ctjkw8VF3Q3H2oyWiR/P0CqDA9UaN+t7NqvvgvJyL1+1YBAtGuyTMep0Lds6KtVz9qKVflOyVtYvS1lkuMA6FpMRWg1zAvPoH/4+x1i18Gl4aLnzG+zlrtYELnnSBVNdR3o8PlDaXggl3QUNhs9b9zdbr4l8RxnJVXSHAv053OWTf3VKwtb2ysz/a9eD4m492bObuD8e3LR3jgutz/p53j6S/+j/gihhO/nfuvYWZw3dVglSMVfxXua7r3V4+1lv+XuJuOByffcHKxv3cUjE5FdSc/bp7X+2Sj/eU0hXckk+tB7u1GT+60C3d0+MWV2i6BDj9Aihw03FvnQbVm8LYq12EnHVIL3EXfHOju5sf8ERgztn9Rje8sKIAU1ElZ9tmuerjeSU5FoZyVV339Kof/B/KWvOzyxkY8Lh7oyyuykdCo76BqZzs1fZ5bsJH9xu9HVezFVz8knsNeV2OIi/rfEtw+FuPyxjX07B1Bkx5yiXbTn3KzboM5KSAirVcELXmZ7fOW3Yyg782lwbuvFIwnUa6/5foOf4fs3uF+5o1gTxTk7Ncj2d2pVcOxrrh8K6joEL1/LW3ShQMHw13zncdFzf+DoOeK9o81QA6PQMocP+xN/4O5/3HJR+/0w9+uMOVPPj8cld24KoxbuHUQGh8hhtDnvECpKUE5jnFObLfLSDcqF9Rt+S4zGG8zdPy3vfoIde1XbuD6/Es7loNcsN4gVzM1h8rvnZ3y+0u835sp6vcygEzX4Z57wauTevGu8RwLyVQzrjPlz/ynMvXjCjjJsEE+kPnjPtd9fzxD8KeDac+vtX3oVrMix2WKK0Hu/puy8b4f0zmDLza2QRQUT1cHtTmP099bN7bbqSmz535a2tWNVu5m8qGvQLXixoCSs5vkh+lyro3q3uXQu873Bvwr39zSXpXfhbYVaKNgXP/6T5UlnwWuOfNy7Y5rjft5Q5udtbBnYV37sISPRewoRVAtbkEqjaCn+7Ou/r1n8/AwR2uF6SwFysNhsyekszel8KQkQ6rf4IW57uJIvlx0YvQ+mK3fMfctwueCJ98wC1/4bUafEQZGPIGXPq6S3a/a2HB0wiyExYOl73nzpddWYdts6BsVTesKKGhdAXoMNzVJvS3l3f3SrceZnYToSJKu/y8LScFUMkH3WSQtkNCoxZWiDq9A6hM5SNh4FPwyFZ4aBM8vCn79aoKqvl57sX653OFs3r9kX3w5ZWuF6R2W5dg+3r3wpm6nZ29m12OSqArs2+b5XKHokJoVe8yFd1MuMRYF0Tl9GGcuMv1eHS+puiWoAm0yKZQs7XrfSks0XPc1Oj8lhsBF7xePtoFURMfha+ugd//4XKQsqtnk5eNf7jZbvlZTscY6HqtS3YPZjHUKvXh/P+6NSRPHsrbNssNx5agHoMS4YKnXED99fUw8XGY9mzuN2m7Vmbf+5Sp6VluFYfEXce3Lf7ELfTsT8X705j+MrIqXQEq1Aje9G9j3JDh4ThXqTXYQ3mzX3d3wdf+6Iou3jnfTWP++oaCL0zp1aof4LUuMP4B94e/1EMXdF62zXL1l3JaYqCoRHWDc/7h8kxyWg9x3jvuQ7b/A4XbtmBrPdgNAZ1cJT1YVn3vhu9aDizY85QqCyM+c0mum/90we0Pt8PbvrwuL71S6ya4wq6hFNhnp/M1LlF80j+Or5hwMNbd8IRSr644ZSrC1V+7oGjhhzDtf/D97dm/NtOOuirk2eU/ZWpylvuamW6QlgJz3oLG/f2bjHEaUwBV2Br2gkteg02T3RtzsKohJ+52wxDtLz9eQTiyiatgvW+Lu8MuTLNfc7OL7pjncip+vMvdoRdU0l7YuTR03+j73OVymyY+emqv49FEWPChG+7zukxQqOt4pcufKMj6Xf7KHL5reUFgbn7Cwlwy/+Mx8Pfdbi00mw5fXgUfX+yKleYlPRU2/A4tLwz9asthYa7afcphGHOFe11mJhU3DtG/q9Ndhepwy2T4+y5XcHf9hOxz9+LXutdubj1QdTq60hYzX3Gv22VfQuJO6Hdv0JpfUiiAKgpdR7m1uVZ+49Y4C0bRwfnvQloynP34idsbn+GmkS4bm/PU1UDbudSVhehxsyvmduXnrj7Irw/nb2gkq1W+khRtQ3SmUHiEy206uAOm/u/ExxZ+5BY/7lsC36hqtnI1ZnKa4eWPtKMuHzGvv481P7le3Q4j8n+unBjjAtw75rr6WjEL4Ifb8m7Tttmu97cwl9MpiNrtYMSnELvcVbVfMBpKVzq1dpCEnl5/desjTvoH7Nt24mPHlnDJ5f8xLMzNjItf4/JkJzzseiRLQKXwYFMAVVT63ed6J+a/51ZLD2RPlLWw8jtocibUaH7q4+0vd8NGgegB8seij9zwSqcr3c9lKrkAcu8mb7NJspOfKrmFrUFPV7hw7ptuWjq4HsLpL7iZKYGcrBBKOo10syMz38T9lfn6faMHvN4VPrnEBeA57TvrVZd3FcxgJbyUWwZm4FOuZ2nOm7nvv26Cq7PUbEDw2hRoLQe6HurD8W79uyZnloxJDSWdMW6ZHozLr81q90r33pvX+qCtBrlcveVfud6oq74sMaUGgsmvAMoYs9UYs8IYs9QYs9C3LdIYM8kYs8H3tVpwm1rCGOOW6+h+o/sA+OovgUss37XcDdPllFAb1d2trF0YtXqOJroFZttfDuWyvERaDXJ5S9OegdTk/D13wiZXJbfTVaH/x37B/0HNNm6dux2L4PcnIO2Im2VVUrW/HMIivK2Nl5YCP98D39wAZSq7RXDj1sD757jcve3zT+y13DoTdi5xNyOFMVTW42aXZD75v3AgJvt9rHUJ9E3PLvzldAqq/eVw3wq4ezEMfauoWyP+qhLlXpvLxpy4ZNiuFW4CkT9/G4NfdJ9Ho77Pf92n04yXHqgB1trO1trMjMhHgcnW2hbAZN/P4kVYmBsWGPQcrJ8Iv9wXmOG8VT+4atA5LfsQFu7uNjdMcmPewbRxsltgtvPIE7cbA+f+yw1t/XhH/nrgln8FmLzXGAsFpcvDFR+5SvTvn+NKZvS7N/sewpKiQnVv6wJa624kFn8K/R+Ev/7plny4Z4lbv279RBh9PjzfzN1pb5nuhhvK14DOVwf91wHc6/bCpwF76t1+pi3TXbHDgswILErGuJy8clWLuiXiRf8HXE2niY+4mwxrfUu45JL/lFXlei4XLqc1JOUUBRnCGwJ84vv+E2BogVtzOjLGjWGf/bj7UC3o7DRr3RIiTfrnfhfR6iKXf7NtdsHOl5d1E1zPU4Pepz7W9Cw3K3Hlt67+lpcg6nCCG/5sfp77wy8OarVx668Ne9fN8ur/YFG3KPhaXeR6Q+PX5b3viq/d8NjAp13NtMy75rKV3TIm969ypSEa9XPVuT+5xC2cPOSNwp2BWbWhu1Nf8nn2C7suHO1e81qAVwpThRpuOadNU1wv7r4trrxBbvlPUiD+BlAW+N0Ys8gYk1kquba1Ntb3/S6g+K8MWJT6P+Cmjf76t4JN/Y5d5qYf5/Xm3XSAy9FYH8Rih+lpsOE3aDEw51yKfve6WiMLR7u6O/6WV/jjn27fCwK89EawVazphhwHPB56ZReCoeWF7uu6PNbGO7IffnvCDev2ui37fcpHQvvLYOQYt4rAJa+53qmiSNTu/6Crz/THv0/cnrjLra/Z+S+uJIJIYep+I5z1qFt39TXfuqAKoILG3wDqDGttV2AQcKcx5sysD1prLS7IOoUx5lZjzEJjzML4+PiCtbYkCwuHS151Qzzz3sn/8ywf5xayzWv19NLloX53t35YsGyf6+6AcvuAM8YV8rvwWRfMvdnbJYbn1hu1daa7++9zp+vVkdBVpT7U7Zx3vt2U/4OkPW5I25/CjQ17Qbfrii7HqGItOOMBNwMw62SMhR+6CRpe1+MTCZSzH3UFYc/7txuSiyohBXpDkF8BlLV2h+9rHPA90BPYbYypC+D7GpfDse9Za7tba7vXrFkzMK0uqao3c1OmF47OX6HL9DRYMQ5aXZh92f6TRXVzSYZpR72fyx/rJrgK4XlVdTcGet8G1493w47f/xXePRPW/35qmYO4ta6nKrIpnPlwcNotgdV6sJv+fyjbtwiXBL7gA1deIxhLlgRLv3sgshmM/5tb+mLteJj+vFt8t6TV9ZLiwxi33MsZ97tAXpXkgybPK2uMqWCMqZT5PXABsBL4CbjOt9t1wI/BauRppd+9rn7M4nysl7dpipuC3Glk3vuC64FKT3FBVDCsm+DqTpWp5N/+jfrALdPc3dPRA66o33NNXcC04AO3ZMGnQ1xQds13+V/zTApXq4sA65aHOFlGOvxyv+vROeeJQm9agUSUcTOX9m1xr9Nx10G9LjD07aJumYgUAn+KfNQGvjdumngEMMZaO9EYswAYZ4y5CdgGBKGK3Wkoqjs07OvqzPS8xdWf8deyL6FcJDQ/3/9zAcQsDPxyE3s3uzpPPW/Ne9+swsLc3VObS1zezKYpsHGKbykU43JkLnnVVVWX4qFOe/f/Of1FV+yyWqPjj81+zfVAXT7aLTNU3DQbADf+Bmt/gQM74KIXFNiLnCbyDKCstZuBTtlsTwCCsOKu0O9etwjwqu+ho59xadJeN4TQ7Tq3wrY/KteDSvXc8Ao5JO7mV2bByPxWs40o46aBtxvmZhbu2+JWhvdnaFJCz4XPwMaeruzAyLFumGH7fJj8pPs/bn95Ubcw/xr2dv9E5LSiwdFQ1OICqNEKZr3mf12opWMg/Sh0vS7vfbOK6gY7FnpvY142ToZqjQOTC2KMy3lS8FR8VYlyMw/XT3SLSS8b64Zmq9R3PYqhXghVROQkCqBCUVgY9L0bdq9wiw7nxVo3+6dBr9xX3c5OVA/YtxUO7/HezpyKcKYddcUEm5+nD0Y5rs+driL7mp/cRIGKtdySEcVx6E5ETnsKoEJVxxFQpeHxGT652fKnyzfqfpP389T35T5tn+/f/ilJMOFReKUjPNvE5U+dLHqOqz6uxSglK2PcjcF1P8MVH8Ot070H/CIiIUIBVKiKKAOXvQf7t8H4B3IeystIhz+f91U+zqP2U3bqd3Pl/zdPzXvf9FQYdy3Mf9et3l6hOnwx3JUWyCqzfEHj/t7bIyVf4zNc3pOmV4tIMaZ3sFDWqM/xJV7GP5j9kNnMl2DbTFeMMj+Vj0uVdYFO1mKAORn/AGyc5IqzjfwSRv3gAqWvrjleryn1iJsN2PpizUYSEZESy58yBlKU+j8IRw+66d47FrrZSpHNXMJ49FxYMBraD4cuo/J/jubnuSVXEjblnPS9b6urTdX7Duh2vdsW2cRVjv7qLy5o6jrKzRxMPqBKzCIiUqIpgAp1YWFuvbe6nWD6CzDpn8cfiygHrS9yPUIFSdZucR5MwM2cyymAWvSJO0efO0/c3now1OsKfz7r8rYWfgjVW7hhGhERkRJKAVRx0WG4+5e4Cw7tBhPmApVALFga2dT92/gH9Mqm8GVaCiz5zC0MWyXqxMeMgXP/CZ8NhRdbw5G9MPBpzb4TEZESTQFUcVOpjvsXaM3Pc0N0qcmnBmVrf3FLxOQ0y6/p2S4Ha+9mKFUBul4b+PaJiIiEEAVQ4rQcCPPfc/WbWl5w4mMLP4SqDaHZOdkfa4yrni4iInKa0Cw8cRr3h9IV3fpzWcWvh60zoNsNmnYuIiLio09EcSLKQPNzXQ2njIzj2xd9BGGlCjbLT0REpIRRACXHtRoMh3ZB7BL3c+oRWPoFtL0UKtYs2raJiIiEEAVQclyL88GEw5pfXOXziY/5ajrlY4kYERGREkxJ5HJc+Ug3o27myy7vKWYBnHE/NO5X1C0TEREJKeqBkhMN/9At+Bq73FUcP/dfRd0iERGRkKMeKDlRuaqu8vmAxyGirApiioiIZEMBlGSvVLmiboGIiEjI0hCeiIiIiEcKoEREREQ8UgAlIiIi4pECKBERERGPFECJiIiIeKQASkRERMQjBVAiIiIiHimAEhEREfFIAZSIiIiIRwqgRERERDxSACUiIiLikQIoEREREY8UQImIiIh4pABKRERExCMFUCIiIiIeKYASERER8UgBlIiIiIhHCqBEREREPFIAJSIiIuKRAigRERERj/wOoIwx4caYJcaYX3w/NzHGzDPGbDTGfGWMKR28ZoqIiIiEDi89UPcCa7L8/CzwsrW2ObAPuCmQDRMREREJVX4FUMaYKGAw8IHvZwOcA3zj2+UTYGgQ2iciIiIScvztgXoFeBjI8P1cHdhvrU3z/RwD1A9s00RERERCU54BlDHmYiDOWrsoPycwxtxqjFlojFkYHx+fn6cQERERCSn+9ED1Ay41xmwFxuKG7l4FqhpjInz7RAE7sjvYWvuetba7tbZ7zZo1A9BkERERkaKVZwBlrX3MWhtlrW0MXAVMsdb+BZgKDPftdh3wY9BaKSIiIhJCClIH6hHgAWPMRlxO1OjANElEREQktEXkvctx1tppwDTf95uBnoFvkoiIiEhoUyVyEREREY8UQImIiIh4pABKRERExCMFUCIiIiIeKYASERER8UgBlIiIiIhHCqBEREREPFIAJSIiIuKRAigRERERjxRAiYiIiHikAEpERETEIwVQIiIiIh4pgBIRERHxSAGUiIiIiEcKoEREREQ8UgAlIiIi4pECKBERERGPFECJiIiIeKQASkRERMQjBVAiIiIiHimAEhEREfFIAZSIiIiIRwqgRERERDxSACUiIiLikQIoEREREY8UQImIiIh4pABKRERExCMFUCIiIiIeKYASERER8UgBlIiIiIhHEUXdABERESneUlNTiYmJITk5uaibki9ly5YlKiqKUqVK+X2MAigREREpkJiYGCpVqkTjxo0xxhR1czyx1pKQkEBMTAxNmjTx+zgN4YmIiEiBJCcnU7169WIXPAEYY6hevbrn3jMFUCIiIlJgxTF4ypSftiuAEhEREfFIAZSIiIgUawMGDOC33347Ydsrr7zC7bffzoUXXkjVqlW5+OKLA3pOBVAiIiJSrI0cOZKxY8eesG3s2LGMHDmShx56iM8++yzg51QAJSIiIsXa8OHDGT9+PCkpKQBs3bqVnTt30r9/f84991wqVaoU8HPmWcbAGFMWmA6U8e3/jbX2X8aYJsBYoDqwCBhlrU0JeAtFRESk2PjPz6tYvfNgQJ+zbb3K/OuSdjk+HhkZSc+ePZkwYQJDhgxh7NixjBgxIqiJ7f70QB0FzrHWdgI6AxcaY3oDzwIvW2ubA/uAm4LWShEREZFcZB3Gyxy+C6Y8e6CstRY45PuxlO+fBc4BrvZt/wT4N/B24JsoIiIixUVuPUXBNGTIEO6//34WL15MUlIS3bp1C+r5/MqBMsaEG2OWAnHAJGATsN9am+bbJQaoH5QWioiIiOShYsWKDBgwgBtvvDHovU/gZwBlrU231nYGooCeQGt/T2CMudUYs9AYszA+Pj5/rRQRERHJw8iRI1m2bNkJAVT//v254oormDx5MlFRUaeUO8gvT2vhWWv3G2OmAn2AqsaYCF8vVBSwI4dj3gPeA+jevbstYHtFREREsjV06FBc5tFxM2bMCMq58uyBMsbUNMZU9X1fDjgfWANMBYb7drsO+DEoLRQREREJMf70QNUFPjHGhOMCrnHW2l+MMauBscaY/wOWAKOD2E4RERGRkOHPLLzlQJdstm/G5UOJiIiInFZUiVxERETEIwVQIiIiIh4pgBIRERHxSAGUiIiIFGsDBgw4pb7TK6+8wqBBg+jTpw/t2rWjY8eOfPXVVwE7p6c6UCIiIiKhJnMdvIEDBx7bNnbsWJ577jnq1q1LixYt2LlzJ926dWPgwIFUrVq1wOdUD5SIiIgUa8OHD2f8+PGkpKQAsHXrVnbu3En//v1p0aIFAPXq1aNWrVoEalUU9UCJiIhI4Ex4FHatCOxz1ukAg57J8eHIyEh69uzJhAkTGDJkCGPHjmXEiBEYY47tM3/+fFJSUmjWrFlAmqQeKBERESn2MofxwA3fZV0PLzY2llGjRvHRRx8RFhaY0Ec9UCIiIhI4ufQUBdOQIUO4//77Wbx4MUlJSXTr1g2AgwcPMnjwYJ566il69+4dsPOpB0pERESKvYoVKzJgwABuvPHGY71PKSkpDBs2jGuvvZbhw4fn8QzeKIASERGREmHkyJEsW7bsWAA1btw4pk+fzscff0znzp3p3LkzS5cuDci5NIQnIiIiJcLQoUOx1h77+ZprruGaa64JyrnUAyUiIiLikQIoEREREY8UQImIiEiBZR06K27y03YFUCIiIlIgZcuWJSEhoVgGUdZaEhISKFu2rKfjlEQuIiIiBRIVFUVMTEzAlkkpbGXLliUqKsrTMQqgREREpEBKlSpFkyZNiroZhUpDeCIiIiIeKYASERER8UgBlIiIiIhHCqBEREREPFIAJSIiIuKRAigRERERjxRAiYiIiHikAEpERETEIwVQIiIiIh4pgBIRERHxSAGUiIiIiEcKoEREREQ8UgAlIiIi4pECKBERERGPFECJiIiIeKQASkRERMQjBVAiIiIiHimAEhEREfFIAZSIiIiIRwqgRERERDzKM4AyxjQwxkw1xqw2xqwyxtzr2x5pjJlkjNng+1ot+M0VERERKXr+9EClAQ9aa9sCvYE7jTFtgUeBydbaFsBk388iIiIiJV6eAZS1NtZau9j3fSKwBqgPDAE+8e32CTA0SG0UERERCSmecqCMMY2BLsA8oLa1Ntb30C6gdg7H3GqMWWiMWRgfH1+QtoqIiIiEBL8DKGNMReBb4D5r7cGsj1lrLWCzO85a+561tru1tnvNmjUL1FgRERGRUOBXAGWMKYULnr6w1n7n27zbGFPX93hdIC44TRQREREJLf7MwjPAaGCNtfalLA/9BFzn+/464MfAN09EREQk9ET4sU8/YBSwwhiz1LftceAZYJwx5iZgGzAiKC0UERERCTF5BlDW2pmAyeHhcwPbHBEREZHQp0rkIiIiIh4pgBIRERHxSAGUiIiIiEcKoEREREQ8UgAlIiIi4pECKBERERGPFECJiIiIeKQASkRERMQjBVAiIiIiHimAEhEREfFIAZSIiIiIRwqgRERERDxSACUiIiLikQIoEREREY8UQImIiIh4pABKRERExCMFUCIiIiIeKYASERER8UgBlIiIiIhHCqBEREREPFIAJSIiIuKRAigRERERjxRAiYiIiHikAEpERETEIwVQIiIiIh4pgBIRERHxSAGUiIiIiEcKoEREREQ8UgAlIiIi4pECKBERERGPFECJiIiIeKQASkRERMQjBVAiIiIiHimAEhEREfFIAZSIiIiIRwqgRERERDxSACUiIiLikQIoEREREY/yDKCMMR8aY+KMMSuzbIs0xkwyxmzwfa0W3GaKiIiIhA5/eqA+Bi48adujwGRrbQtgsu9nERERkdNCngGUtXY6sPekzUOAT3zffwIMDWyzREREREJXfnOgaltrY33f7wJq57SjMeZWY8xCY8zC+Pj4fJ5OREREJHQUOIncWmsBm8vj71lru1tru9esWbOgpxMREREpcvkNoHYbY+oC+L7GBa5JIiIiIqEtvwHUT8B1vu+vA34MTHNEREREQp8/ZQy+BOYArYwxMcaYm4BngPONMRuA83w/i4iIiJwWIvLawVo7MoeHzg1wW0RERESKBVUiFxEREfFIAZSIiIiIRwqgRERERDxSACUiIiLikQIoEREREY8UQImIiIh4pABKRERExCMFUCIiIiIeKYASERER8UgBlIiIiIhHCqBEREREPFIAJSIiIuKRAigRERERjxRAiYiIiHikAEpERETEIwVQIiIiIh4pgBIRERHxSAGUiIiIiEcKoEREREQ8UgAlIiIi4pECKBERERGPFECJiIiIeKQASkRERMQjBVAiIiIiHimAEhEREfFIAZSIiIiIRwqgRERERDyKKOoGSOHaGJfIh7O2smrnQSqXjWB4tyiGdK5f1M0SEREpVhRAnUbmbk5g5PtzKR0eRvfG1dix/wj3jl1KdEISd53THGNMUTdRRESkWFAAdRr5cekOKpSOYNpDZ1OjYhlS0jJ45NvlvDhpPdUqlOaa3o2KuokiIiLFgnKgThPWWqaujad/ixrUqFgGgNIRYbx4RSf6t6jB07+uIWZfUhG3UkREpHhQD1QRs9YyfcMePpy5hVLhYTSILEfrOpVoWrMitSqVoV7VcpQKL3icuyY2kV0HkxnQutYJ28PCDE9f1oGBL0/n0W9X8OmNPQkLy34ob/Ka3Xw5fzsLtu5lYLva/G1gK2pVKlvgtomIiBQ3CqCKUEaG5e6xSxi/PJZ6VcpSuVwpZm/aQ1JK+rF9SoUbWtauxGVdoxjRPYpKZUvl61xT18UBcHarmqc8FlWtPH+/uC2PfbeClyat528DW53weFp6Bs9OXMv7M7ZQp3JZ+jarzvdLdvDHmjimPXQ2lfPZJilZDian8suyWGZt2kNUtXL0aBTJuW1qKbdOREokBVBFaPTMLYxfHss957bgrgHNKR0RRkaGJXpvEtv2JhF3MJlN8YeZuzmBJ39ZzdvTNvLxDT1pX7+K53NNXRtHh/pVcuwxuqpHA5bH7OeNqRtpUqMCl3eLAiA1PYO7xizmt1W7ubZPI/5xcVtKhYcxfX081344n5kb9nBRh7oFug5S/M3ZlMBtny/iwJFUalcuw6RVqbz752Y6RlXhf8M65Os1GyqSU9OZszmBOpXL0rpOJQWEIgIogCoyy7bv57nf1jKwXW3uP6/FsTflsDBD4xoVaFyjwgn7L92+nzu/WMzI9+by0Q096N440u9zLd2+n8XR+7hrQPMc9zHG8J9L27Nlz2Ee/HoZK3ce4NzWtfl0zlZ+X72bf17clhvPaHJs/77NqlO5bATT1sUVOIBKOHSUlTsPEp94lOoVStOzSSQVyhTvl2Z6hmXv4RRqVCxd4j9wxy+P5f6vltKwenk+vqEHnRtUJS3D8uPSnTz/21qu/2gBv957RrEb7rXW8urkDbw/fTOHfb3CDSPd79i0ZsUibp2crg4cSWVJ9D4OJqfRp2l1alYqU9RNOm0Za22hnax79+524cKFhXa+UJSRYfluyQ7+8cNKqpUvxfh7+lOtQmm/jt25/wjXfDCPfUkp/HJPf+pXLZfnMbsPJnPJ6zMpHRHGT3edQWQe5zqals7Tv67l49lbj207OXjKdOcXi1mwdS/zHj831yBhxoZ4vlu8g7mbE0hOTadMRDh1q5aldHgYB46ksnZX4gn7lwo3XNenMX8b2IqypcLz/B1DgbWWmH1HmLI2jp+X7WTFjgMcTcugdZ1K3NivCSN6NCjqJgbFwq17Gfn+XDpFVWX0dT2oUv7E4dz1uxO59I2ZdGtUjU9v7EV4Dvl1WVlr2b73CGFhUKNimSJ7Dbzz5yaemeBuckb2bEjcwaM89esaWtauyFe39skxV1AkWBZs3ctdYxaz++BRABpVL8/3d/TL8X3dWsuqnQc5kppO90bVSvzNXDAYYxZZa7tn+5gCqODYvjeJmRv3sCb2IEkp6SSnpnP4aBrLYg6w93AKvZpE8vrILtSq7O2ufHP8IS59YxbNalVk3F97UyYi5w+XXQeSueHjBWxLOMx3d/SldZ3Kfp9n1c4DHD6aTt0qZWkQWT7bfb5euJ2HvlnO+HvOoF29U4dorLW88scGXp28gSrlSnFWy5pUKVeKpJR0Yg8cIT3DUq50ON0bVaNH40jqVClLzL4j/LBkB18viqFpjQq8dU1XT+0uLHGJyXw2ZxvfLd7B0bR0UtMtB46kAtC6TiXOaF6D6hXL8MvynazaeZCnL+vAyJ4Ni7jVgRWzL4khb8yiUtkIfrizH1XLZ/8m/tWCaB75dgUjukfx9GUdcwyilm3fz+dztzF1XTx7DrkPiNLhYQzvHsWdA5r7dcMQCOkZljenbuSlSeu5pFM9Xr2y87FgadzC7Tz8zXKeHNKOUX0aF0p7AiHh0FEWbdvH6tiDrIk9yK4DyTSILM+g9nUZ3FFD8MXBVwuiefz7lURVK8d/h7QnLT2DO75YTPv6Vfji5l6n3GisiDnAXz9byM4DyYB7X3picBv6tzg1D1ZypgCqkH0+dxv/+HEl1kKlshFUKhNB2VLhlCkVTps6lejfsgaXdKxHRD5n101YEcvtXyzmim5RPDe8Y7Z3Fat2HuCmjxeSmJzK29d048yWgf+jiUtMpudTk3loYCvuzGZ48LmJa3lr2iaGd4viqWHtcw32TjZr4x7u/2oph46m8eIVnRgUQnlWaekZXPDKdLbsOczZLWtSz/fB3rpuZXo1iaRl7UrH9k3PsFz/0Xzmbd7Ll7f2plujakXV7IA6cCSVK96ZTez+ZL6/sx/Na+U8pGWt5eU/NvDa5A2c07oWQ7vUp37VslgLsQeSWR17kD9W72ZD3CEqlA7nvLa16dE4klLhhqXb9/PNohgqlIlg3F/7nHBt8+toWjqx+5M5kppOeobFWkhJTycxOY0Nuw/x68pYlkTvZ0jnejw/vBOlI47/nVprGTV6Pkui9zHpgbOO/d97EZ94lDmbE9ix7wiDO9SlYfXsb1ACITU9g49nbeXlP9aTlJKOMdC4egXqVS3LprjD7E5M5v1R3Tmvbe2gtUHyZq3lnT83M2dzAgmHjtKpQVXOalmTfs1rkJ5h+XT2Vl6ctJ7+LWrw5l+6Hpu4M355LHeOWcwjF7bm9rObHXu+5NR0Br82g8NH0/nbwFZkWMs70zYRvTeJF67oxNAuWn3CX0ELoIwxFwKvAuHAB9baZ3LbvygCKGstOw8ks/tgMtsSDrM2NpE9h1JITc+gR5NIBrat7bkXKLdzvTVtE8//to5zWtfiicFtaFqjQlC6TV/6fR2vTdnIwxe24o6zTwxeJq/Zzd1fLqFKuVJ8eH0P2tQNXg/Oxa/P4EhKOj/edQYVs+Qt/b5qF7d+toirejTgf8M65Gu4I+5gMrd9vogl2/fzwvBOxxLbi9r3S2K4/6tlvPWXrn7lf+1PSuGSN2YSERbG7/efWeCyFGnpGYSHmSLrjk9Jy+D6j+azYOtePrmhJ32b1/DruA9mbOa539aRkpZxwvbwMEPPxpFc1KEOQ7vUP2Wm6eb4Q1z53lwM8O3tfXPsEc3Lyh0HePKX1czfupfc3vaiqpXj/vNaclnX+tle4+17k7jg5en0aVad0dd19/T/sHrnQS57exbJqe4aGAOXd43i6cs6BKRcSVZJKWnc+ukiZm7cw7mta3H72c1oU7fysfzCIynpjHh3DpvjD/HDnf1oEYDgVPLnw5lb+O8vq2ldpxI1K5Vh8bZ9HE5Jp1S4IcO6G7GLO9blpRGdTwjoAa77cD7LY/Yz45Fzjr0H/98vq/lg5hY+v6kXZ7Rwf5+Jyanc+uki5mxO4JUrOyuI8lNQAihjTDiwHjgfiAEWACOttatzOqYwA6j0DMvElbt4d/omlsccOLa9dEQYNSuWIcNaYg8kUzo8jNvOasodA5oXKNfCWsszE9by7vTNDO1cj+ev6BTwN8STz3fP2KX8vGwnV/VowGOD2lCmVBgv/7Ge96dvpm29yoy+rge1AxQc5mTaujhu+mQh/ZrXYPR13SkVHkbMviQufn0m9auW47s7+nrqeTrZkZR0bvl0IbM27eG1q7pwSad6AWy9d+kZlgte/pNS4WH8ek9/vwPDyWt2c9MnC/M99GOtZdzC7XwxL5q1sYm0qF2R/xvani4NC7dHy1rLg+OW8d2SHbx4hfegNiUtgw1x7iYGoHblMjSuXiHPv711uxIZ8e4cmtSowLe39/UrlwpcL+mk1bv5bdVuZmyIp1r50ozs2YAmNSpSoXQ4YWGGMGMoFW6oWCaChtXL+5XsPnrmFp78ZTWvXtXZ77Ukk1PTufSNmexLSuW9Ud2oWakMn8zeyvsztnBRhzq8dlWXfPdKn2zXgWTuGrOYxdH7ePqyDozo3iDbQC/2wBEGvzaTdvUq89lNvQJybvFm0bZ9XPnuHAa0rsV7o7phjCElLYNF2/YxfUM8EWGGM1vWzDGHadn2/Qx5c9axkYCPZ23h3z+vZlTvRjw5tP0J+yanpnP9R/NZvG0/Y27p5WkyUm7W707ky/nuvSkpJY2rezXM8TWXE2stczfvZfqGeLbvTaJC6Qha1K5Iy9qVaF23UpFNQglWANUH+Le1dqDv58cArLVP53RMYQRQ0QlJTFqzm0/nbGVbQhKNq5fnmt6NaFqzAlHVytO0RgUiwsOw1rIx7hBvTN3Ij0t30qp2JT64rnu+7m6TUtJ4/LsV/LB0J9f2acS/L2lXKAmmR9PSeWmSC5gyLESEGdIyLFf1aMA/L2lL+dKFM5MtM8elduUytKtXhZkb9hARbvj57jNoFoDZSsmp6YwaPY81sYlMvK8/UdWCN+SRlx+X7uDesUt58+qunnJHrLVc+d5cNscfYtpDA07orctLXGIy//hhJb+t2k37+pXp0TiSCSt2sTsxmVeu9P8DvKCstTz/2zremraJB85vyT3ntiiU82bKvPZ/H9yGm/s3zXG/2ANHGL88lokrd7Eoeh/WumTbizvW5dYzm1GlXMHrlqVnWK54Zzbrdx/ix7v6+fU6z+wV+PiGHpzd6nhB2w9mbOb/xq9hWJf6vHhFJ7/fO6ITknj422Xs2H8EgI5RVWlcvTyJyWl8vTCGdGt55crOefaSZibL/3BnPzo3qOrXufOSlu4CgOi9SVQpV4qmNSvSrGZweuPzI+HQUSas3EWNimXo0bga1SsWzUy25NR0Br4ynQxr+eXu/vl+bd78yQKmr99Dm7qVWBZzgIHtavP6yK6n9FaB6xEf9tZsDh5JZcJ9/fMdmGT+nf28bCfLYg5QOjyMDlFVSEpJZ03sQYZ3i+KZyzr4dVOwcOtenvxlNctiDhARZqhfrRyHktNIOJxybJ+OUVUY1qU+V/dqWKCbcq+CFUANBy601t7s+3kU0Mtae9dJ+90K3ArQsGHDbtu2bcvX+fzxn59X8dGsrQB0alCV285sygXt6uR5tzptXRz3fLmEUuFhfHxDTzpE+V+zZlP8IW7/fBEb4g7xwHkti2RR3hUxB/hzfRwHk9OOjZsXtt9X7eK7xTtYseMA57WpxfX9mtDkpFIMBbF9bxKDXp1BB1/CZFHMgDpwJJXzX/qTGhXL8MvdZ3huw5LofQx7azZdG1blv0PaU6VcKRZH7+PHpTtZtyuR+MSjNIgsR4f6VWhfvwrVK5YmOuEIH8zYzNG0DB4a2IqbzmhCWJghMTmV6z9awPpdiUy8/8ygJ1jHJx7lse+W88eaOK7q0YCnL+tQ6K9zay23fLqQmRv38OOdZ9CqzolDTit3HODlSeuZui6ODAtt6lZmYLvaXNi+Dq1qB75+0879R7jk9ZlUq1CaH+7sl2tQHJ2QxDkvTuMKXyL9yd6YsoEXfl/PzWc04YnBbfJsa1xiMle8M4f9SakMaFWT1HTLkuh97E48SrgxnN+2No8Oau3XDeGho2mc8ewUujWsxujre+T9i+dh9sY93P3lkhM+/AAiK5Tmog51GNmzYbaTTgpDWnoG//t1LZ/P3UZKuhtGLRMRxphbiiY/MTNPdMwtvejbLP/v27sPJvP2tE0sj9lPu3pV+OclbXMdAdmwO5HBr89kQKuavHNNN7//NvYeTmH8ilh+XrqT+Vv3AtC+fmUu7VSPy7tGUb1iGdIzLK/+sZ7XpmzkrgHNTynMnFVKWgb/+3UNn8zZSr0q5bhjQDMu7xp1rEc64dBRNsQdYnH0Pn5buYtlMQeoX7Ucw7rUp0vDqvRsEpnv4tL+KtIAKqtg90DN2BDPht2HOKd1rVPqKOVlU/whrh09n+TUdL69va9fx49fHsvD3yyjdEQYr17VJSiJ2nJcZk/Xy1d2YlgX/4aOrLXM37KX9XGHuKBt7QINaT7yzXK+WRzDj3f2y3dhyB+X7uBfP61if1LqsW31qpSld9Pq1KhUhs3xh1mxY/+xacoA/VvU4D+Xtjul9lB0QhKDXp1Oh6gqjLm5d8CDypS0DKasjePbxTFMXRtHmDE8dlFrru/buMh6EjLLcgB8c1tfGlYvT8Kho7w5dRMfz97iG6JryPBuUZ7fA/Jj9sY9XDN6Hhe2r8ObV3fN8bo8MG4p45fHMv3hAdm+Bq21/Ofn1Xw8eyuPDmrNbWc1y+ZZnJS0DIa/M5sNuw/xxS296BqAYdzXJ2/gxUnr+emufnSMqprv59kYl8iwt2ZTu3JZHji/Je3qVebAkVTWxB5k5sYEfl+1i5T0DG7p35QHL2hZqD0JR1LSuWvMYiavjePK7g24vl9jDh9N496xSykVbhh/T/9CrT+3eudBLn1jJsO61Of5KzoV2nkzZfY8+jMMvWF3Iq9P2cjEle7/r3mtilzaqR4Xd6ybY020R75ZzrhF2/n0xp7ZzvxLTE7l9s8XM3PjHq7t04hHLmyd5/WfsSGeV//YwJLt+0nPsHx3R9+AvP5zc1oN4RXEpvhDDH97NpXLleKHO/rlWp/p/embeerXNXRpWJU3r+6ar9k44k1GhuXCV6cTZgwT7u2f54f4xrhD3PrZQjbHHwZcsvLIng34z6Xt/c6hyfTzsp3c/eUSbjurGY8Oap3v3wFgz6Gj/LoilrIR4TSqXp4ejSNPCX7iEpNJTE4jsnzpXF+HX86P5rHvVvD2X7oGdKbiH6t389j3K4hPPErNSmUY1qU+V/ZoEJAh2YJav9vlQ6VnWFrWrsTqnQdJTkvnqh4NefTC1qfUogq296Zv4n+/ruWxQa35azaBz8a4RC54eTo3ndGEJwa3zfF5MjIs937l8hqfH96RK7pnXzvsxd/X8fqUjbxzTTcubF8nIL9DYnIqA174k4aR5fjmtr75CsaPpLjhqKSUNH64s1+2Q+0HjqTy3MS1fDEvmk4NqvLR9T3yrE2Xlp7B+BWxzNq4h+i9SZQrFU5UtfJ0aViVPs2qU7eKf++9j3+/gi/nR/PfS0/MQ5y7OYGR78/19aye2jsYDInJqQx5YxaHjqbx231n+l0LMJDSMyzD35nNyh0H+Ocl7bimV8NT3lMz00RGz9hCuVLhXN4tiit7NPCrIv+RlHSGvDmTmH1H+O+Q9lzum5RhrWXK2jie/GU1MfuO8PRlHXJ8ref23Ct2HKBTgypBD8KDFUBF4JLIzwV24JLIr7bWrsrpmFAPoAAWR+/jqnfn0qtpJB/f0DPbD9pX/9jAy3+sZ3CHurx0ZadCvYs63WXWnvrkxp6clUuP35Y9h7ny3TlkWMtjg9rQrn5lPp+7jc/nRnsegpqxIZ4bP15AlwbV+PSmniFV3DMzqT3MGCbed6bnwHBT/CE27D7EvqQUwgwcPJLGnM0JTFkbR5u6lXl4YCv6t6gRsOTmQFm98yAfzdrCtoQkGkSW5/azm9K8VtHMIrPWcteYJUxYGcsbV584MzMtPYOr35/Hqp0HmP7wgDxzbVLSMrjpkwXM2riH57OZebokeh/D35nDsC71eSHAvRaZf1v5mRwA8Pa0TTw7ca1fw1ETV8Zy79ilRFUrx2c39crxBnTRtr08OG4ZWxOSqF6hNI1rVCA5NZ3ohCQSj6YB0KtJJKP6NOKi9nVzDPzW707kwlemc22fxvz70nanPP7MhLW88+cmnr28A1f2KFi9tt0Hk/l91S6mrYunQpmIY7k7mf/3aekZ3Dt2KRNX7WLMzb3o1bR6gc5XEAeSUrnvqyVMXRfPgFY1eWRQ62N196ITkrjry8UsjznAiO5RPHJha8+5YrsOJHPv2CXM27KXxtXL07B6BdbvcgvbN6tZgf8b2oE+zYru9/dHMMsYXAS8gitj8KG19qnc9i8OARTA2PnRPPrdimx7G/5YvZubP13IZV3r8/zwTp4/sKRgUtIy6P/cFJrVrMiYW3pnu8+BI6kMfm0GSSnpfHlL7xNyZV74bR1vTN14St2U7FhrGbtgO//+aRVNalTgq7/2CUgCcqD9snwnd41Z4mlG2IqYA7zw+zr+XB9/ymMNIssxpFN97j63uW4O/JSUksa1o+ezLGY/r4/swsB2dTDG8NKk9bw2eQMvjejEZV39C0qylh944PyW3HhGEyqWieDP9fHc9cViKpcrxYT7+gd8Ee+MDMtlb89mx/4jzHh4gKcbhQNJqfR/bgrdG0fyoZ95VPM2J3DzJwuJrFiasbf2PqUnad7mBG74eAE1K5Xh8YvacH6b2scCpIwMy7rdiUxes5tvFsWwNSGJNnUr8+zlHbIdgrzho/ks3LaPPx8akG2PV1p6Bjd8vIC5mxMYe2uffOVDzdywhxcnrWNJ9H7ATVxIS7fs2H+EcqXCuaxrfTo1qMqYedEs3b4/z6HawpKRYflw1hZenbyBxOQ0GkaWp0KZCNbEHqRSmQheGNGJge3y39OZnmH5Yt42Zm9MYNveJJrXqkj/5jUY1rV+UGeqB4oKaebD49+vYMy8aB48vyV3+2YbbdlzmKFvziKqWjm+vb1vSPVEnE4yZy09d3nHU5ZIySzv8OuKWMb99dQ3Qmstt362iNkb9zD1obNznIGSmJzK49+v5OdlO+nfogYvX9mZGkU0UycvGRmWi16bAcDE+87Mc993pm/ipd/XU6VcKW48owlntaxJZIXSWFxCbaj+nqHuYHIqf3l/nm9ooSoRYYbF0fsY1qU+L43o7Om5klPTeXDcMsaviD1WjDf2YDKt61Rm9HXdg5YyMHODy+ny2gv19IQ1vDd9M7/e099T3bkl0fsYNXo+NSqW5oUrOtG9cSQZGZYv5kfz1PjVRFUrz5hbeuU6Uyw9w/LL8p08N3Ed8YeO8r9hHRiepe2ZxSbzClj2J6Vw6RuzOJKazs93nUGdKv7lS2ZkWF6atJ43p22kUWR5hneLYmC7OjSvVRFjDBvjEnlz6iYmrIwlOTWDquVL8eSQ9lzcsW7IzEoE9/t/syiGhVv3cTA5lb7NqjOkc/18110rKRRA5UN6huWhr129m7Na1qRJjQqMXRBNqfAwfr7rjEJJUJXspWdYrvtwPvO37uW72/uekNCd2Xv4twtactc52U+z3xx/iAtens4V3d1Q3slW7jjAXWMWE703iQcvaMXtZzUL+XXPMgvxTf3b2bnOfnzyl9WMnrmFwR3q8r9hHQo9X6ikS05N5+tFMXw2ZyvlS0dwZosa3HZ2s3yXFFm6fT9fzN1Ghq8Uw01nNAlqorO1lvNfnk65UuH8dFc/vz7g52xK4C8fzOWyrlH5GlZctG0ft3++iLjEo7SuU4mklHSi9ybRv0UNXhrR2e/FcvceTuGuMYuZvSmB6/s25onBbdiWcJghb8yiZZ1KfHVrn2yn9We1blciw96aRYvalfjq1t5+3SRn5sBd0S2K/w5pT7nS2R+Tlp7BpvjD1KlcVn93xYgCqHxKz7C88sd6flq2k20JSQzuUJfHB7cptDW5JGcJh45yyeszSUxO4x8Xt2VIl3pMXLmL+79aSr/mNXLMX8v0n59X8cnsrTxzWUeu6B51rHjdmHnb+N+va4msUJrXRnahZ5PAFJoLtp37j9D3mSnZVqbPNG7Bdh7+djnX923Mvy5pG1J3vxI6Pp+7jb//sJJvb+9Dt0a5v/53H0xm8GszqVIu4pTVCLxISknj49lbmbd5L+VKhTOgdU3PhRjBBSlPT1jL6JlbqFq+FEdTM6hQJpyf7z7D72TziSt3cdvni+jVJJL3r+ue61Dp4uh9jHhnDue1qc3b1+Q8C1OKLwVQAXD4aFqhTnGVvG3fm8Tfvl7GvC17j23r3dTlYOR1x3/gSCp//WwhczfvpUvDqtSuVJYFW/eScDiFs1vV5KURnfOcHRRqhrw5C6zlx7vOOOWxbQmHOe+lP+ndtDofXd8j5JLCJXQkpaTR63+T6dE4MtelajbGHeLGjxcQn3iUH+/qF5B1CgPl1xWx/LkuntIRYVzZo4HnsiM/Lt3Bg+OW0aRGBR68oBXntalFWoYlem8SW/ccJjktg0Vb9/LNohiqVSjN+HvyXwRTQpsCKCmxMjIsv66MZeuew4SFGa7v29jv4ZLM5Mmfl+3kcEo6zWtW5KqeDTizRc2QH7LLTuYsqFmPnnNKL2lmHaIZDw8I2NqPUnK9++cmnp6wNseK+5PX7Ob+r5ZSOiKM96/tXuhLChWGGRvieeL7lUTvTcr28dLhYVzcsS73nNtCKR0lmAIokdPAlj2HGfDCtFOqWW+KP8T5L/2ZZx0ikUxp6RkMe2s2sQeO8PVtfY/l1UUnJDF65mY+mbONtnUr8+6obiU6yTgtPYPfVu1m/e5ESoUbtxxYzQqULx1BzYpllMt0GsgtgNKYlEgJ0aRGBS7vGsUHM7dwOCWdhwa2Ii09g3/+uJKypcJDYsq0FA8R4WE8e3lHhr41iwEvTKNJjQqkpGWwY/8RwgyM6t2IJwa3KfEzkSPCwxjcsS6DCVyRWik5FECJlCDPD+9IrcpleHvaJsYt3E6pcEN6huWfF7ctsgVTpXhqW68yUx48i19XxLJo2z4qlilF05oVuKxrfb8TskVKMg3hiZRAq3Ye4JflsexPSuWvZzZVjoaISD5oCE/kNNOuXpUiW/FeROR0oLnMIiIiIh4pgBIRERHxSAGUiIiIiEcKoEREREQ8UgAlIiIi4pECKBERERGPFECJiIiIeKQASkRERMQjBVAiIiIiHimAEhEREfFIAZSIiIiIRwqgRERERDxSACUiIiLikbHWFt7JjIkHtgX5NDWAPUE+x+lI1zU4dF2DQ9c1OHRdg0PXNTgCcV0bWWtrZvdAoQZQhcEYs9Ba272o21HS6LoGh65rcOi6Boeua3DougZHsK+rhvBEREREPFIAJSIiIuJRSQyg3ivqBpRQuq7BoesaHLquwaHrGhy6rsER1Ota4nKgRERERIKtJPZAiYiIiARV0AMoY0wDY8xUY8xqY8wqY8y9vu2RxphJxpgNvq/VfNtbG2PmGGOOGmP+luV5Whljlmb5d9AYc18O57zQGLPOGLPRGPNolu2jjTHLjDHLjTHfGGMq5nD8U8aY7caYQydtf8D3eyw3xkw2xjQKwCXKlxC7rh8bY7ZkeY7OORzfxBgzz3f8V8aY0r7tZxpjFhtj0owxwwN3lbwLsetqfK/F9caYNcaYe3I4/gvf8SuNMR8aY0plOf413/MuN8Z0DeCl8iTEruu5vtfbUmPMTGNM8xyO72aMWeE7/jVjjPFt7+Rr2wpjzM/GmMoBvFSehNh1Pcd3XVcaYz4xxkTkcHxOr9cqvuu5zPe73BDAS+VJEV3XD40xccaYlSdtv8LXhgxjTI4zynLazxjzl5PakGFyeI8Wj6y1Qf0H1AW6+r6vBKwH2gLPAY/6tj8KPOv7vhbQA3gK+FsOzxkO7MLVZ8jusU1AU6A0sAxo63uscpb9Xso8fzbP0dvX7kMnbR8AlPd9fzvwVbCvXzG5rh8Dw/1o8zjgKt/37wC3+75vDHQEPvXneU6j63qD75qEZZ4rh+e/CDC+f19mua4XARN823sD83RdLb5zt/F9fwfwcQ7PP9933YzvOg7ybV8AnOX7/kbgydP9uuJuxrcDLX37/Re4yePr9fEs7awJ7AVKnw7X1ff4mUBXYOVJ29sArYBpQPdc2pznfkAHYFNRvV5L2r+g90BZa2OttYt93ycCa4D6wBDgE99unwBDffvEWWsXAKm5PO25uBdBdkU5ewIbrbWbrbUpwFjfubDWHgR3Zw6UA7JNALPWzrXWxmazfaq1Nsn341wgKpc2BlUoXVd/+K75OcA32bRtq7V2OZDh7/MFS4hd19uB/1prMzLPlUObf7U+uA/9zNflEOBT30NzgarGmLp5XoQgCLHraoHMXqMqwM6TD/Zdp8q+9wKLC2SH+h5uCUz3fT8JuDyXNgZVCF3X6kCKtXa9b78cr0sur1cLVPK9V1TEBVBpeVyCoCiC64q1djrudz55+xpr7To/2uzPfiNx/2cSAIWaA2WMaQx0AeYBtbMEKbuA2h6e6ircnUt26uPuhDLF+LZltuEj3/laA697OOfJbsLdlRa5ULiuwFPGDRO9bIwpk83x1YH91trMN8STjw85IXBdmwFXGmMWGmMmGGNa5NHeUsAoYKIfz11kQuC63gz8aoyJwV2vZ3I4PiaH41dxPBi7Amjgoc1BU8TXdQ8QkWXoaDh5XJdsXq9v4HpRdgIrgHszbx6KUiFd18JyZQi0ocQotADKuHyjb4H7MnuCMvnuRPyaDmhc3sylwNf5aYe19gagHu6O4sr8PIcx5hqgO/B8fo4PpBC5ro/hAtIeQCTwSD6eI6SEyHUtAyRbV0n3feDDPPZ/C5hurZ2Rj3MVihC5rvcDF1lro4CPcMP5XtwI3GGMWYQb3knJRxsCqqivq+8cVwEvG2PmA4lAeh6Hnfx6HQgsxb0/dwbeKMr8Mij66xpIxpheQJK1dmWeO4tfCiWA8t1pfAt8Ya39zrd5d+Zwgu9rtsMT2RgELLbW7vYd2yBLctxtwA5OvPOJ8m07xlqbjuvGvNwYE57l+P/68bucBzwBXGqtPepnm4MiVK6rr7vb+q7HR7hufowxv/mO/wBIwA0hRZx8fKgJleuKu7vPPP/3uDyxk69rZpv/hcsbeSDLc+X5t1CYQuG6GmNqAp2stfN8278C+mbzPrCDE4fos77e11prL7DWdsPdzW/ydCECLBSuK4C1do61tr+1tiduiHO97zn8fb3eAHzney/ZCGzB3ZgViUK+rvlp30e+43/185BQ6AErUbKdJRFIvvHs0cAaa23WO72fgOtw3efXAT/6+ZQjyfIisNZux92tZJ4vAmhhjGmC+8O+Crja145m1tqNvu8vBdb6gqnO+MEY0wV4F7gwp3yUwhIq19X3WF1rbayvTUOBlb7nGHhSm6fiuvbHemxboQml6wr8gJu4sAU4C98HUjbX9Wbc3fu5Jw15/ATcZYwZC/QCDthscvsKQwhd131AFWNMS1++zvm+Np3yPmDcjKneuKGba/EN+Rtjallr44wxYcDfcRMiikQIXdes16UMrhf6Kd9z+Pt6jcblCc0wxtTGJURv9rPdAVXY1zU/fKMpfvG9VkcA/QtyTjmJDXKWOnAGrptzOa57diluFkZ1YDKwAfgDiPTtXwd3530Q2O/7vrLvsQq4nowqeZzzItyHzSbgCd+2MGAWbmx9JfAFWWblnXT8c77zZvi+/tu3/Q9gd5bf46dgX79Qv66+7VOyXNfPgYo5HN8UlzS6EdeVXca3vYevPYd97Vil62oBqgLjfdd2Dq7nJLvj03zHZrb3n77tBnjT99gKcpnBc5pd12G+67EMN2OpaQ7Hd/e9pjfh8nMyCw/f63ve9bgPUqPrasGlNKwB1uGGvHI6PqfXaz3gd46/l1xzml3XL4FYXCJ6DL5ZjL7XawxwFPf581sOx+e4H3A2MLeormdJ/adK5CIiIiIeqRK5iIiIiEcKoEREREQ8UgAlIiIi4pECKBERERGPFECJiIiIeKQASkRCkjGmqjHmDt/39Ywx3+R1jIhIYVEZAxEJScatQfaLtbZ9UbdFRORkQa9ELiKST88AzYwxS3GFC9tYa9sbY67HVbyvALQAXgBK4xamPYpb526vMaYZrpBoTSAJuMVau7awfwkRKZk0hCcioepRYJO1tjPw0EmPtQcuw1Wxfwq3SGoXXMX2a337vAfcbd2adX/DLV4rIhIQ6oESkeJoqrU2EUg0xhwAfvZtXwF0NMZUBPoCX7tlzQAoU/jNFJGSSgGUiBRHR7N8n5Hl5wzc+1oYsN/XeyUiEnAawhORUJUIVMrPgdbag8AWY8wVAMbpFMjGicjpTQGUiIQka20CMMsYsxJ4Ph9P8RfgJmPMMmAVMCSQ7ROR05vKGIiIiIh4pB4oEREREY8UQImIiIh4pABKRERExCMFUCIiIiIeKYASERER8UgBlIiIiIhHCqBEREREPFIAJSIiIuLR/wMFzUCoo+zjywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_df.groupby('time').sum()[['V1', 'V2']].plot(figsize=(10, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is straightforward to build this forecast using `VARModel` and plot the results as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use VAR model to forecast this multivariate time series\n",
    "from kats.models.var import VARModel, VARParams\n",
    "\n",
    "params = VARParams()\n",
    "m = VARModel(multi_ts, params)\n",
    "m.fit()\n",
    "fcst = m.predict(steps=90)\n",
    "\n",
    "m.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Hyperparameter tuning\n",
    "\n",
    "To identify which hyperparameters to use for a specfied forecasting model, we have classes in Kats that allow you to efficiently identify the best hyperparameters.  Here we will provide an exmaple of how to do hyperparameter tuning for an ARIMA model that using the `air_passengers` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kats.utils.time_series_parameter_tuning as tpt\n",
    "from kats.consts import ModelEnum, SearchMethodEnum, TimeSeriesData\n",
    "from kats.models.arima import ARIMAParams, ARIMAModel\n",
    "\n",
    "from ax.core.parameter import ChoiceParameter, FixedParameter, ParameterType\n",
    "from ax.models.random.sobol import SobolGenerator\n",
    "from ax.models.random.uniform import UniformGenerator\n",
    "warnings.simplefilter(action='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method we use to hyperparameter-tuning is a static method called `create_search_method`.  To call this method, we need to specify the type of search we are doing and the search space for the parameters.  We specify the search space for the parameters by defining a dictionary for each parameter and combining these dictionaries into a list.  Here we are specifying that we want to look at all ARIMA(p,d,q) models where the values p, d, and q are either 1 or 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_grid_search = [\n",
    "{\n",
    "    \"name\": \"p\",\n",
    "    \"type\": \"choice\",\n",
    "    \"values\": list(range(1, 3)),\n",
    "    \"value_type\": \"int\",\n",
    "    \"is_ordered\": True,\n",
    "},\n",
    "{\n",
    "    \"name\": \"d\",\n",
    "    \"type\": \"choice\",\n",
    "    \"values\": list(range(1, 3)),\n",
    "    \"value_type\": \"int\",\n",
    "    \"is_ordered\": True,\n",
    "},\n",
    "{\n",
    "    \"name\": \"q\",\n",
    "    \"type\": \"choice\",\n",
    "    \"values\": list(range(1, 3)),\n",
    "    \"value_type\": \"int\",\n",
    "    \"is_ordered\": True,\n",
    "},\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we are going to create a grid search with these parameters.  The full list of arguments of the `create_search_method` are as follows:\n",
    "* **Parameters:** List[Dict], this is a list of dictionaries, where each dictionary gives the search space for a parameter      \n",
    "* **selected_search_method:** SearchMethodEnum, the type of search method used to do parameter tuning\n",
    "* **objective_name:** str, the nume of the objective function used for the search (this is arbitrary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_tuner_grid = tpt.SearchMethodFactory.create_search_method(\n",
    "    objective_name=\"evaluation_metric\",\n",
    "    parameters=parameters_grid_search,\n",
    "    selected_search_method=SearchMethodEnum.GRID_SEARCH,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have defined our search grid, we need to define the metric we are calculating at each point on the grid.  Given a set of parameters p, q, and d, we define our evaluation function to be mean absolute error (MAE) of the forecast for the test data set (using an 80/20 training-test split) using these respective parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide into an 80/20 training-test split\n",
    "split = int(0.8*len(air_passengers_df))\n",
    "\n",
    "train_ts = air_passengers_ts[0:split]\n",
    "test_ts = air_passengers_ts[split:]\n",
    "\n",
    "# Fit an ARIMA model and calculate the MAE for the test data\n",
    "def evaluation_function(params):\n",
    "    arima_params = ARIMAParams(\n",
    "        p = params['p'],\n",
    "        d = params['d'],\n",
    "        q = params['q']\n",
    "    )\n",
    "    model = ARIMAModel(train_ts, arima_params)\n",
    "    model.fit()\n",
    "    model_pred = model.predict(steps=len(test_ts))\n",
    "    error = np.mean(np.abs(model_pred['fcst'].values - test_ts.value.values))\n",
    "    return error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our grid and our evaluation functions defined, we can display our evaluation metric for each point on the grid using the following function calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arm_name</th>\n",
       "      <th>metric_name</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>trial_index</th>\n",
       "      <th>parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_0</td>\n",
       "      <td>evaluation_metric</td>\n",
       "      <td>115.201849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'p': 1, 'd': 1, 'q': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0_1</td>\n",
       "      <td>evaluation_metric</td>\n",
       "      <td>54.991723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'p': 1, 'd': 1, 'q': 2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_2</td>\n",
       "      <td>evaluation_metric</td>\n",
       "      <td>183.293937</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'p': 1, 'd': 2, 'q': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_3</td>\n",
       "      <td>evaluation_metric</td>\n",
       "      <td>157.331347</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'p': 1, 'd': 2, 'q': 2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_4</td>\n",
       "      <td>evaluation_metric</td>\n",
       "      <td>52.021996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'p': 2, 'd': 1, 'q': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0_5</td>\n",
       "      <td>evaluation_metric</td>\n",
       "      <td>56.346372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'p': 2, 'd': 1, 'q': 2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0_6</td>\n",
       "      <td>evaluation_metric</td>\n",
       "      <td>141.107489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'p': 2, 'd': 2, 'q': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0_7</td>\n",
       "      <td>evaluation_metric</td>\n",
       "      <td>165.195864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'p': 2, 'd': 2, 'q': 2}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  arm_name        metric_name        mean  sem  trial_index  \\\n",
       "0      0_0  evaluation_metric  115.201849  0.0            0   \n",
       "1      0_1  evaluation_metric   54.991723  0.0            0   \n",
       "2      0_2  evaluation_metric  183.293937  0.0            0   \n",
       "3      0_3  evaluation_metric  157.331347  0.0            0   \n",
       "4      0_4  evaluation_metric   52.021996  0.0            0   \n",
       "5      0_5  evaluation_metric   56.346372  0.0            0   \n",
       "6      0_6  evaluation_metric  141.107489  0.0            0   \n",
       "7      0_7  evaluation_metric  165.195864  0.0            0   \n",
       "\n",
       "                 parameters  \n",
       "0  {'p': 1, 'd': 1, 'q': 1}  \n",
       "1  {'p': 1, 'd': 1, 'q': 2}  \n",
       "2  {'p': 1, 'd': 2, 'q': 1}  \n",
       "3  {'p': 1, 'd': 2, 'q': 2}  \n",
       "4  {'p': 2, 'd': 1, 'q': 1}  \n",
       "5  {'p': 2, 'd': 1, 'q': 2}  \n",
       "6  {'p': 2, 'd': 2, 'q': 1}  \n",
       "7  {'p': 2, 'd': 2, 'q': 2}  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameter_tuner_grid.generate_evaluate_new_parameter_values(\n",
    "    evaluation_function=evaluation_function\n",
    ")\n",
    "\n",
    "# Retrieve parameter tuning results\n",
    "\n",
    "parameter_tuning_results_grid = (\n",
    "    parameter_tuner_grid.list_parameter_value_scores()\n",
    ")\n",
    "\n",
    "parameter_tuning_results_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the calculations in the table above, we can conclude that ARIMA(2,1,1) has the minimal error of 52.02."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Backtesting\n",
    "\n",
    "Kats provides a backtesting module that makes it easy to to compare an evaluate different forecasting models.  While our hyperparameter tuning module allows you to compare different sets of parameters for a single base forecasting model, backtesting allows you to compare different types of base models (with pre-specified parameters).  \n",
    "\n",
    "Our backtesting module allows you to look at multiple error metrics in a single function call.  Here are the error metrics that are currently supported:\n",
    "* Mean Absolute Error (MAE)\n",
    "* Mean Absolute Percentage Error (MAPE)\n",
    "* Symmetric Mean Absolute Percentage Error (SMAPE)\n",
    "* Mean Squared Error (MSE)\n",
    "* Mean Absolute Scaled Error (MASE)\n",
    "* Root Mean Squared Error (RMSE)\n",
    "\n",
    "Our example below shows how you can use the `BackTesterSimple` class to compare errors between an ARIMA model and a Prophet model using the `air_passengers` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.utils.backtesters import BackTesterSimple\n",
    "from kats.models.arima import ARIMAModel, ARIMAParams\n",
    "\n",
    "backtester_errors = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we define a backtester to look at each of the supported error metrics for an ARIMA(2,1,1) model.  We specify in the `BackTesterSimple` definition that we are using a 75/25 training-test split to train and evaluate the metrics for this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = ARIMAParams(p=2, d=1, q=1)\n",
    "ALL_ERRORS = ['mape', 'smape', 'mae', 'mase', 'mse', 'rmse']\n",
    "\n",
    "backtester_arima = BackTesterSimple(\n",
    "    error_methods=ALL_ERRORS,\n",
    "    data=air_passengers_ts,\n",
    "    params=params,\n",
    "    train_percentage=75,\n",
    "    test_percentage=25, \n",
    "    model_class=ARIMAModel)\n",
    "\n",
    "backtester_arima.run_backtest()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we run the backtester, the `errors` attribute will be a dictionary mapping each error type name to a its corresponding value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "backtester_errors['arima'] = {}\n",
    "for error, value in backtester_arima.errors.items():\n",
    "    backtester_errors['arima'][error] = value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we run another backteseter to caluculate the same error metrics for a Prophet model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    }
   ],
   "source": [
    "params_prophet = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results\n",
    "\n",
    "backtester_prophet = BackTesterSimple(\n",
    "    error_methods=ALL_ERRORS,\n",
    "    data=air_passengers_ts,\n",
    "    params=params_prophet,\n",
    "    train_percentage=75,\n",
    "    test_percentage=25, \n",
    "    model_class=ProphetModel)\n",
    "\n",
    "backtester_prophet.run_backtest()\n",
    "\n",
    "backtester_errors['prophet'] = {}\n",
    "for error, value in backtester_prophet.errors.items():\n",
    "    backtester_errors['prophet'][error] = value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can compare the error metrics for the two models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arima</th>\n",
       "      <th>prophet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mape</th>\n",
       "      <td>0.120354</td>\n",
       "      <td>0.073343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smape</th>\n",
       "      <td>0.124584</td>\n",
       "      <td>0.069898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mae</th>\n",
       "      <td>54.348743</td>\n",
       "      <td>29.219968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mase</th>\n",
       "      <td>2.674938</td>\n",
       "      <td>1.438149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mse</th>\n",
       "      <td>5052.300405</td>\n",
       "      <td>1103.835150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rmse</th>\n",
       "      <td>71.079536</td>\n",
       "      <td>33.224015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             arima      prophet\n",
       "mape      0.120354     0.073343\n",
       "smape     0.124584     0.069898\n",
       "mae      54.348743    29.219968\n",
       "mase      2.674938     1.438149\n",
       "mse    5052.300405  1103.835150\n",
       "rmse     71.079536    33.224015"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(backtester_errors)"
   ]
  }
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